Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes (...) of making computers understand medical natural language. (shrink)

In this article, we present a new approach for frequent pattern mining that runs fast for both sparse and dense databases. Two algorithms, FEM and DFEM, based on our approach are also introduced. FEM applies a fixed threshold as the condition for switching between the two mining strategies; meanwhile, DFEM adopts this threshold dynamically at runtime to best fit the characteristics of the database during the mining process, especially when minimum support threshold is low. Additionally, we (...) present optimization techniques for the proposed algorithms to speed the mining process, reduce the memory usage, and optimize the I/o cost. We also analyze in depth the performance of FEM and DFEM and compare them with several existing algorithms. The experimental results show that FEM and DFEM achieve a significant improvement in execution time and consume less memory than many popular FPM algorithms including the well-known Apriori, FP-growth, and Eclat. (shrink)

One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages. Video is an illustration of interactive media data since it contains text, pictures, meta-data, visual, (...) sound, and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data. It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far-reaching method to association rule extraction from video databases. (shrink)

A large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In recent times, researchers have reported an algorithm for finding calendar-based periodic pattern in time-stamped data without considering the purchased quantities of the items. However, most of the real-life databases are nonbinary, and therefore, exploring various calendar-based patterns with their purchased quantities may discover information useful to improve the quality of business decisions. In this article, a (...) technique is proposed to extract calendar-based periodic patterns from nonbinary transactions. In this connection, the concept of certainty factor has been introduced by incorporating transaction frequency for overlapped intervals. Algorithms have been designed to mine frequent itemsets along with intervals and quantity. In addition to that, we have designed an algorithm to find the periodicity of the pattern. The algorithm is tested with real-life data, and the results are given. (shrink)

Since the second half of the twentieth century, biomedical research has made increasing use of epidemiological methods to establish empirical evidence on a population level. This paper is about practices with data in epidemiological research, based on a case study in Denmark. I propose an epistemology of record linkage that invites exploration of epidemiological studies as heterogeneous assemblages. Focusing on data collecting, sampling and linkage, I examine how data organisation and processing become productive beyond the context of their collection. The (...) case study looks at how a local population database established in 1976 to investigate possibilities for the prevention of cardiovascular disease is used thirty years later to test hypotheses on the aetiology of breast cancer. For two breast cancer investigations based on the same core data set, I follow the underlying record linkage practice and describe how research objects such as molecular markers become relevant with respect to public health through information networking. Epidemiological association studies function as tools that performatively enrol different contexts into statistical risk estimation, thereby configuring options for research as well as for clinical testing and public health policy. (shrink)

Discovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find profitable itemsets that (...) are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM|$_{all\text{-}confidence}$| and FCHM|$_{bond}$|⁠, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

Privacy concerns involving data mining are examined in terms of four questions: What exactly is data mining? How does data mining raise concerns for personal privacy? How do privacy concerns raised by data mining differ from those concerns introduced by traditional information-retrieval techniques in computer databases? How do privacy concerns raised by mining personal data from the Internet differ from those concerns introduced by mining such data from data warehouses? It is argued that the (...) practice of using data-mining techniques, whether on the Internet or in data warehouses, to gain information about persons raises privacy concerns that go beyond concerns introduced in traditional information-retrieval techniques in computer databases and are not covered by present data-protection guidelines and privacy laws. (shrink)

The academic databases such as Scopus or Web of Science are commonly used to measure performance of universities, departments, and even single researchers. However, to what extent such databases can represent real outcomes of aforementioned units especially in the field of art and humanities where local languages and cultural phenomena play an important role is not clear. This article focuses on understanding how research in this field, as seen through the case of aesthetics in non-English speaking countries, Finland in particular, (...) is represented through major academic databases. This question is tackled by applying a data mining approach. First, we identify major academic databases, and afterwards test what is the proportion of Finnish aesthetics presented in those databases. Our approach allows us to critically look into representation of local art or humanities related research in general academic databases, and understand to what extent we can trust those sources in representing real pictures of the field. Results strengthen the well-known fact that most common academic databases draw a rather poor picture. In more particular this article shows that there are certain factors that influence analysis of the field: spread data, multilingual content, non-standard categorization, variability of venues, different publishing patterns, and unsuitable impact metrics. However, our results propose that alternative databases can be formed from university or meta-data databases. We also provide further directions for designing of such databases. (shrink)

The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique (...) of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm. (shrink)

Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past. Consequently, a number of machine learning techniques, mostly supervised, have been proposed to extract keyphrases from text documents. But, one of the main bottlenecks that hinders the success of such systems is the requirement of annotated corpora for training (...) purpose. In this paper, we propose the design of a deep text mining system to identify keyphrases in text documents that are either unstructured or semi-structured in nature. The novelty of our system lies in its applicability on a single document, instead of demanding a collection of annotated texts for training, to identify keyphrases embedded within it. The proposed system applies parsing techniques to identify candidate phrases. After mapping the original set of candidate phrases into a low-dimensional space using Singular Value Decomposition, the Markov Clustering technique is applied to cluster related sentences together. Finally, considering each cluster as a document, Latent Dirichlet Allocation is applied to identify feasible keyphrases that are presented to users in non-increasing order of their relevance score values. The efficacy of the proposed system is established through experimentation on datasets from two different domains. On comparative evaluation, we found that the proposed system outperforms KEA and KEA that apply the supervised machine learning approach for automatic keyphrase extraction from text documents. (shrink)

The present study examines certain challenges that KDD (Knowledge Discovery in Databases) in general and data mining in particular pose for normative privacy and public policy. In an earlier work (see Tavani, 1999), I argued that certain applications of data-mining technology involving the manipulation of personal data raise special privacy concerns. Whereas the main purpose of the earlier essay was to show what those specific privacy concerns are and to describe how exactly those concerns have been introduced by (...) the use of certain KDD and data-mining techniques, the present study questions whether the use of those techniques necessarily violates the privacy of individuals. This question is considered vis-à-vis a recent theory of privacy advanced by James Moor (1997). The implications of that privacy theory for a data-mining policy are also considered. (shrink)

In recent years, with the development of construction industry, more scientific, systematic, fast, and intelligent calculation methods are needed to coordinate urban development and fierce market competition, and mathematical algorithm library plays an important role in artificial intelligence. Therefore, the author uses computer mathematical algorithm and extension theory to study and analyze the residential building design and intelligent data mining. It is found that the research of the computer-aided expression method of extension building planning is mainly the expression of (...) the input and output system of extension building planning. It includes knowledge representation, system outline design, system flow, and interface expression based on the mathematical database. (shrink)

Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation algorithm has been proposed with novel grouping and load balancing (...) strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup. (shrink)

In order to improve education and teaching methods and meet the “heart” needs of college students in the era of big data, this paper analyzes the application of data mining technology in college mental health education, and introduces database technology and decision tree algorithm to support college mental health work. This process verifies the feasibility of this kind of system with the help of an example. Using the test standards outlined in this document, 1.5 previous test tasks were (...) completed within the timeframe. During the system test, the error rate was 14% and the number of tests was 7%.However, the error rate in the development stage is 11%, which is lower than 19% of the old version. The error rate in the acceptance stage is 14%, which is lower than 5% of the old version. That is to say, most of the errors were found in time in the system analysis and design stage. 14% of the problems found in the development stage are basically small problems in the interface display, which do not need major changes. However, the old version also includes design defects found in the development stage, and only large-scale rewriting of the involved modules. In the research process, the work of mental health in Colleges and universities has been promoted. At this time, the law of psychological changes of college students has been summarized. Therefore, the support of data mining technology can better meet the needs of mental health education in Colleges and universities. (shrink)

Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in (...) data analysis. (shrink)

International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis and built decision trees (...) (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n= 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies. (shrink)

Data mining technology is an effective knowledge mining and data relationship induction technology based on massive data, which is widely used in data analysis in many fields. In order to improve the utilization effect of students’ performance and meet the teaching needs of modern education, data mining technology can be applied to the existing performance database to mine the data information and treatment. Data mining technology is used to analyse and process the data stored in (...) the student achievement management system, which provides the basis for improving the teaching quality and optimizing the teaching resources. Based on the analysis of the relevant data of large-scale English test results, this paper finds out the relevant rules that affect college English test results, forms the corresponding performance prediction rules, uses data mining technology to more comprehensively analyse the factors that affect students’ performance, establishes a model, and uses data mining tools to mine and analyse students’ English test data. It is of great practical significance to select the model with high accuracy, further optimize the parameters, make good use of the data, and then take targeted measures to guide the teaching reform, help students make more efficient learning plans, and improve and perfect the existing problems in teaching. (shrink)

This article responds to two problems confronting social and human sciences: how to relate to digital data, inasmuch as it challenges established social science methods; and how to relate to life sciences, insofar as they produce knowledge that impinges on our own ways of knowing. In a case study of proteomics, we explore how digital devices grapple with large-scale multiples – of molecules, databases, machines and people. We analyse one particular visual device, a cluster-heatmap, produced by scientists by mining (...) data from a large number of experiments on human blood plasma proteins. These proteins make up a myriad multiple whose identity shifts in many ways. Rather than displaying data about proteins, the heatmap constructs a view of the differences and similarities between experiments. We find this attempt to construct a view on many things at once instructive in thinking about multiples more generally. Instead of flattening molecular ‘life itself’, this visual device superimposes layers of digital devices and techniques from a wide variety of disciplines. This layering suggests a different way of relating to the life sciences more generally: rather than what they know, how they know might be of use to social and human sciences when attending to multiplicities. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent and (...) informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent and (...) informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is designed by (...) using the product based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. (shrink)

In coal mining technology systems, it is very important to acquire, store, and represent basic geological data comprehensively and accurately. Based on the current working mode and information level in mining geology at coal mines, this paper proposes a process of building basic geological database for modeling of coal mines by using existing results’ data of mining geology and develops the efficient program for getting the basic geological data from the important 2D plane drawings’ achievement at (...) mines, such as the contour maps of mine coal seam floors, geological cross-sections, underground drilling results, and geological survey results, based on AutoLISP, which is a programming language for the secondary development of AutoCAD. The obtained data in general text format is stored and managed by the MongoDB database, which realizes the storage, query, analysis, and correction of massive data of geological objects in the space of the underground coalmine. The application results show that compared with the previous data acquisition methods such as manual input and graphic transformation attribute, the extraction of spatial and attribute data from the existing mine 2D plane drawings by programming can effectively avoid the prominent problems such as artificial gross error, distortion of graph conversion, and different database structure, make the obtained spatial geological data more comprehensive, accurate, and effective, and, meanwhile, increase the rate by more than 60%, which plays an important role in data support for the construction of the geological modeling systems for transparent mines. (shrink)

Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed (...) to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising. (shrink)

Public’s concern about poor animal welfare provided by intensive farming systems has increased over the last decades. This study reviewed the interest of the scientific research on the pain issue in pig production to assess if the societal instances may be a driving force for the research activity. A literature search protocol was set up to identify the peer-reviewed papers published between 1970 and 2017 that covered the topic of ‘pain in pigs’ using Scopus®, database of Elsevier©. One hundred (...) and thirty papers were selected and they were mainly focused on the practice of castration followed by tail docking. The scientific community first focused on these painful practices as a way to improve production efficiency and quality issues while more recently, due to the increased pressure by the public opinion, turned its interest towards the search of alternative solutions. A text mining analysis on the abstract of the selected papers clearly indicated the effort of the research to explore solutions to alleviate pain. Evocative words of this target were the selected terms ‘pharmacological analgesic’ and ‘anaesthetic treatments’. The text mining highlighted vocalizations as the main pain indicators in pigs as this term was frequently associated to ‘acute stress’. Ethical issues were a minor research topic in the scientific literature on pig breeding but in the short run, they are supposed to become a major subject to justify the acceptance of the modern production systems at the eyes of the consumers. (shrink)

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence (...) such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)

This article proposes a new bi-directional way of understanding the convergence of biology and computing. It argues for a reciprocal interaction in which biology and computing have shaped and are currently reshaping each other. In so doing, we qualify both the view of a natural marriage and of a digital shaping of biology, which are common in the literature written by scientists, STS, and communication scholars. The DNA database is at the center of this interaction. We argue that DNA (...) databases are spaces of convergence for computing and biology that change in form, meaning, and function from the 1960s to the 2000s. The first part of the article shows how, in the 1980s, DNA sequencing shifted from passively incorporating computers to be increasingly modeled in digital coding and decoding. Information retrieval algorithms, reciprocally, were altered according to the peculiarities of DNA in the first sequence-storage databases. The second part of the article investigates the impact of these reciprocal interactions and globalization on the organization of research centers, ways of conducting big science, and scientific values. Through convergence and new technologies such as data mining, biology and computing were transformed technologically, institutionally, and culturally into a new bio-data enterprise called genomics. (shrink)

Early Childhood and Neuroscience is a practical guide to understanding the complex and challenging subject of neuroscience and its use (and misapplication) in early childhood policy and practice. The 2nd edition has been updated throughout and includes three new chapters on: - the effects of childhood trauma - school readiness - neurodiversity It also includes a new Foreword by Laura Jana (Penn State University, USA). The book provides a balanced overview of the debates by weaving discussion on the opportunities of (...) using neuroscience in early childhood practice with examination of the limitations and ethical implications throughout the chapters. This enables students to inform their own opinions about the discipline and its use in their future practice. Clear explanations of the main terms and theories are complemented with illustrative case studies of cutting-edge research from around the world, a glossary of key terms and suggestions for further reading. Reflective discussion questions give students the chance to apply their theoretical knowledge to real-world contexts. (shrink)

Having a good grasp of theories of child development and what these look like in practice, can make a positive difference to how you understand babies and children and the ways in which they learn. This guide offers easy access to a wide range of concepts, as well as traditional and current theories, of how babies and children learn. Each chapter offers clear guidance on how to recognise the theory in action within the setting and suggests ways to test these (...) ideas out in early years' settings, supporting the development of reflective practice. Case studies are included throughout, along with questions to guide thinking and encourage readers to develop their practice. Summaries conclude each chapter offering a quick reference of the theory examined and the benefits of applying it to practice. An essential guide for those looking to develop their practice in supporting child development within the Early Years. (shrink)

オリンピック招致問題、部活絶対主義、スポーツへの逃避、無気力試合...... スポーツにかかわる問題の解決に求められている姿勢とは?スポーツに関して、自分の意見に会わない人や新しい情報に接し、自分とは違う考えを取り入れることが、スポーツを理解する素晴らしいヒントになる。複雑な真 実から目をそらさず、学びを止めない姿勢が、今、求められている。.

西洋哲学に対峙した日本の二人の思索者は無や直観、時間や場所などの主題をめぐり何を共有し、どうすれ違ったのか。明晰な最新研究。.

Nurses who attend patients with psychiatric disorders often encounter ethical dilemmas and experience difficulties in making the right decision. The present study aimed to evaluate the decisions of psychiatric nurses regarding their duty to warn third parties about the dangerousness of the patient, the need for compulsory hospitalization, and the competence of patients. In total, 111 nurses working in the field of psychiatry in Turkey completed a questionnaire form consisting of 33 questions. The nurses generally assessed the decision-making competency of (...) the patient correctly. However, their decisions regarding whether the patient should be compulsorily hospitalized and their understanding of their duty to warn/protect were less consistent. A significant relationship was found between the decisions of the psychiatric nurses and their work experience, them having children, and them having postgraduate education in psychiatric nursing. The nurses stated their desire to be part of the team that decided on ethical problems in psychiatry. (shrink)

This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information related to a user query, an agent uses two histories : a query/retrieved document history(Q/RDH) and a query/sender agent (...) history(Q/SAH). The former is a list of pairs of a query and retrieved documents, where the queries were sent by the agent itself. The latter is a list of pairs of a query and sender agents and shows ``who sent what query to the agent''. This is useful to find a new information source. Making use of the Q/SAH is expected to cause a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads to perform a search. As an agent receives more queries, then more links to new knowledge are achieved. From this behavior, a ``give and take''(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agents Kodama which has been developed in our laboratory, and conducted preliminary experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing 'broadcast' techniques only to look up a target agent. (shrink)

Objective To compare assessments of the decision-making competencies of psychiatric inpatients as provided by physicians, nurses, relatives and an assessment tool.Methods This study was carried out at the psychiatry clinic of Kocaeli University Hospital from June 2007 to February 2008. The decision-making competence of the 83 patients who participated in the study was assessed by physicians, nurses, relatives and MacCAT-T.Results Of the 83 patients, the relatives of 73.8% of them, including the parents of 47.7%, were interviewed during the study. A (...) moderately good consistency between the competency assessments of the nurses versus those of the physicians, but a poor consistency between the assessments of the physicians and nurses versus those of the patients’ relatives, was determined. The differences in the competency assessment obtained with the MacCAT-T versus the evaluations of the physicians, nurses and patients’ relatives were statistically significant.Conclusions Our findings demonstrate those physicians, nurses and the patients’ relatives have difficulty in identifying patients lacking decision-making competence. Therefore, an objective competence assessment tool should be used along with the assessments of physicians and nurses, both of whom can provide clinical data, as well as those of relatives, who can offer insights into the patient's moral values and expectations. (shrink)