Abstract
A computer-assisted model for diagnosing jaundice has been adapted for use on the University of London C.D.C. 7600 computer via an on-line terminal at King's College Hospital to provide a rapid turn-round time. The model was used prospectively in the diagnosis of 219 patients--135 seen in a specialized liver unit and 84 seen in one of four district hospitals in south-east London--with an overall accuracy in distinguishing among 11 different causes of jaundice of 69% and 62% respectively. These figures rose to 77% and 88% respectively when only those patients in whom the final diagnosis reached a "certain" probability were considered. When used to distinguish between a medical and a surgical cause of jaundice the accuracy was 86% in the liver unit and 77% in the district hospitals, rising to 95% in both series for those with a diagnosis of certain probability. The proposed improvements to the model--namely, the use of two deparate data bases and more diagnoses within the matrix--should be improve the accuracy even further. In practice the rapid feedback to the clinicians looking after patients provided help in managing difficult cases.
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Selected References
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- Anderson J. A., Boyle J. A. Computer diagnosis: statistical aspects. Br Med Bull. 1968 Sep;24(3):230–235. doi: 10.1093/oxfordjournals.bmb.a070641. [DOI] [PubMed] [Google Scholar]
- Dudley H. A. Pay-off, heuristics, and pattern recognition in the diagnostic process. Lancet. 1968 Sep 28;2(7570):723–726. doi: 10.1016/s0140-6736(68)90764-2. [DOI] [PubMed] [Google Scholar]
- Fraser P. M., Franklin D. A. Mathematical models for the diagnosis of liver disease. Problems arising in the use of conditional probability theory. Q J Med. 1974 Jan;43(169):73–88. [PubMed] [Google Scholar]
- Horrocks J. C., McCann A. P., Staniland J. R., Leaper D. J., De Dombal F. T. Computer-aided diagnosis: description of an adaptable system, and operational experience with 2,034 cases. Br Med J. 1972 Apr 1;2(5804):5–9. doi: 10.1136/bmj.2.5804.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Knill-Jones R. P., Stern R. B., Girmes D. H., Maxwell J. D., Thompson R. P., Williams R. Use of sequential Bayesian model in diagnosis of jaundice by computer. Br Med J. 1973 Mar 3;1(5852):530–533. doi: 10.1136/bmj.1.5852.530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- NUGENT C. A., WARNER H. R., DUNN J. T., TYLER F. H. PROBABILITY THEORY IN THE DIAGNOSIS OF CUSHING'S SYNDROME. J Clin Endocrinol Metab. 1964 Jul;24:621–627. doi: 10.1210/jcem-24-7-621. [DOI] [PubMed] [Google Scholar]
- OVERALL J. E., WILLIAMS J. M. Conditional probability program for diagnosis of thyroid function. JAMA. 1963 Feb 2;183:307–313. doi: 10.1001/jama.1963.03700050039009. [DOI] [PubMed] [Google Scholar]
- RINALDO J. A., Jr, SCHEINOK P., RUPE C. E. SYMPTOM DIAGNOSIS. A MATHEMATICAL ANALYSIS OF EPIGASTRIC PAIN. Ann Intern Med. 1963 Aug;59:145–154. doi: 10.7326/0003-4819-59-2-145. [DOI] [PubMed] [Google Scholar]
- Stern R. B., Knill-Jones R. P., Williams R. Clinician versus computer in the choice of 11 differential diagnoses of jaundice based on formalised data. Methods Inf Med. 1974 Apr;13(2):79–82. [PubMed] [Google Scholar]
- de Dombal F. T., Leaper D. J., Staniland J. R., McCann A. P., Horrocks J. C. Computer-aided diagnosis of acute abdominal pain. Br Med J. 1972 Apr 1;2(5804):9–13. doi: 10.1136/bmj.2.5804.9. [DOI] [PMC free article] [PubMed] [Google Scholar]