Literature DB >> 7026718

The role of modeling methods in medical diagnosis.

D Lezotte, P A Scheinok.   

Abstract

Modeling methods in medical diagnosis are concerned with medical information processing as it pertains to utilizing biological modeling methods to facilitate patient care. Major considerations in this particular area are (1) the classification problem related to the establishment of disease entities-the taxonomy problem, and (2) the diagnosis of diseases. Available are properties, criteria, signs, symptoms, and manifestations of diseases that have been cumulated and categorized by clinicians and researchers. The problem is to optimally utilize the information content of a sign or set of signs in the practice of patient care as pertaining to the medical diagnosis problem. Some mathematical approaches implemented to facilitate such analyses include cluster analysis, discriminant analysis, Bayesian methods, computer approaches, game theory, information theory, stochastic representations, stepwise procedures, decision analysis, and pattern recognition techniques. Each of these has been studied in depth by numerous researchers advocating computer applications in medicine. Here we discuss the scope and limitations of utilizing modeling methods as a viable approach to interpreting vast amounts of biological data collected on a single patient during an encounter. We consider the following: (1) limitations associated with modeling methodologies; (2) levels of responsibilities, ranging over logging, summarizing, reporting, monitoring, and therapy selection; and (3) operational strategies and considerations as they affect hardware logistics, the actual algorithm utilized, and implementation of these sophisticated analysis systems.

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Mesh:

Year:  1981        PMID: 7026718     DOI: 10.1007/bf02221995

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  23 in total

1.  Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason.

Authors:  R S LEDLEY; L B LUSTED
Journal:  Science       Date:  1959-07-03       Impact factor: 47.728

2.  Current concepts of "normal values," "reference values," and "discrimination values," in clinical chemistry.

Authors:  F W Sunderman
Journal:  Clin Chem       Date:  1975-12       Impact factor: 8.327

3.  Algorithm-derived, computer-generated interpretive comments in the reporting of laboratory tests.

Authors:  T H McConnell; C T Ashworth; R D Ashworth; C R Nielsen
Journal:  Am J Clin Pathol       Date:  1979-07       Impact factor: 2.493

4.  Judgment based on 95 per cent confidence limits: a statistical dilemma involving multitest screening and proficiency testing of multiple specimens.

Authors:  I Schoen; S H Brooks
Journal:  Am J Clin Pathol       Date:  1970-02       Impact factor: 2.493

5.  A proposal for laboratory data reporting.

Authors:  R R Grams
Journal:  J Med Syst       Date:  1979       Impact factor: 4.460

6.  Influence of statistical method used on the resulting estimate of normal range.

Authors:  A H Reed; R J Henry; W B Mason
Journal:  Clin Chem       Date:  1971-04       Impact factor: 8.327

7.  Unlimited volumes of laboratory data: a confusing and diagnostically deceptive product of modern technology.

Authors:  R R Grams; D Lezotte
Journal:  J Med Syst       Date:  1978       Impact factor: 4.460

8.  Primer on certain elements of medical decision making.

Authors:  B J McNeil; E Keller; S J Adelstein
Journal:  N Engl J Med       Date:  1975-07-31       Impact factor: 91.245

Review 9.  Mathematical approaches to the analysis of laboratory data.

Authors:  J R Beck; F A Meier; H M Rawnsley
Journal:  Prog Clin Pathol       Date:  1981

10.  Cognitive dissonance. The meaning of discordant imaging results.

Authors:  C C Jaffe
Journal:  JAMA       Date:  1979-10-19       Impact factor: 56.272

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