Medical decisions, including diagnosis, prognosis, and disease classification, must often be made on the basis of incomplete or unsatisfactory information. Data which are essential to the care of one patient may be unobtainable for technical or ethical reasons in another patient. For this reason the principles of controlled experimentation may be impossible to satisfy in human studies. In this paper, some formal aspects of medical decision making are discussed. Special operators for the intuitive concepts of certainty, demand, and effort, akin (...) to the operators of modal logic, are used to accommodate the technical and ethical limitations on human studies. Theorems are stated and proved which show how this system handles incomplete information. The embryogenesis of the human heart is presented as a sample problem in classification. (shrink)

Sadegh-zadeh [23] has proposed a theory of the relativity of medical diagnosis in terms of the time at which a diagnosis is accepted, the patient to whom the diagnosis applies, the physician who renders the diagnosis, the medical knowledge used, the diagnostic method applied, and the set of patient observations. Use of classical formal logic as the diagnostic method may result in three paradoxes: the paradoxes of consistency, completeness, and justifiable ignorance. These paradoxes may be resolved by the addition of (...) two non-classical operators, the certainty and effort operators, akin to the non-classical operators of modal logic. (shrink)

In the past, hypothesis testing in medicine has employed the paradigm of the repeatable experiment. In statistical hypothesis testing, an unbiased sample is drawn from a larger source population, and a calculated statistic is compared to a preassigned critical region, on the assumption that the comparison could be repeated an indefinite number of times. However, repeated experiments often cannot be performed on human beings, due to ethical or economic constraints. We describe a new paradigm for hypothesis testing which uses only (...) rearrangements of data present within the observed data set. The token swap test, based on this new paradigm, is applied to three data sets from cardiovascular pathology, and computational experiments suggest that the token swap test satisfies the Neyman Pearson condition. (shrink)

It has been demonstrated that death certificates do not accurately record the actual cause of death in up to one-fourth of cases, as determined from subsequent autopsy findings. The purpose of this study was to explore the use of natural language autopsy data bases as an automated quality assurance mechanism. We translated the account of the major process leading to death, or the primary diagnosis, from all 45,564 narrative autopsy reports obtained at The Johns Hopkins Hospital between May 28, 1889, (...) and June 30, 1987, into the hierarchical system of Medical Subject Heading (MeSH) titles. We obtained a total of 125,772 MeSH title translations, 1,563 of them distinct (average 2.8 per case), ranging in frequency from 6,029 occurrences of LUNG to 1 occurrence apiece of 357 MeSH titles. The natural-language-to-MeSH translations showed expected trends over the past century: fewer infectious diseases; more cardiovascular and neoplastic disease among adults; and more respiratory diseases and congenital malformations in the pediatric age group. The greater availability of autopsy documents in electronic form should increase the value of this resource for quality assurance. (shrink)