PMID: 11552912Sep 13, 2001Paper

When William of Ockham meets Thomas Bayes: finding a few diagnoses among a great many symptoms

Alimentary Pharmacology & Therapeutics
A Sonnenberg

Abstract

Bayes' formula is a means to estimate disease probability based on the presence of symptoms and the outcome of clinical tests. The probability helps to decide among competing diagnostic options. If, however, several diseases present with similar symptoms, they may appear equally probable, and Bayes' formula will fail as an aid to reach a diagnostic decision. The aim of this study is to show how a merger of Bayes' principle with that of Ockham can help to decide in favour of one diagnosis among multiple, seemingly equally probable diagnostic hypotheses. The hypotheses are compared to each other with respect to those tests and symptoms which they fail to explain. The unexplained tests and symptoms are used to estimate the probabilities for a set of secondary diagnoses that match each one of the primary diagnoses. The more likely a secondary diagnosis appears, the less likely its corresponding primary diagnosis will remain as the sole diagnosis to explain all the clinical findings. Even without a detailed calculation, the proposed concept of using unexplained tests and symptoms to rate competing differential diagnoses could help the clinician to select the most probable diagnosis.

References

Oct 5, 1978·Computers and Biomedical Research, an International Journal·D G Fryback
Jan 29, 1987·The New England Journal of Medicine·S G Pauker, J P Kassirer
Jan 31, 1998·The Surgical Clinics of North America·R T Wilcox, L W Traverso

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Citations

Nov 5, 2003·Gastrointestinal Endoscopy·Amnon Sonnenberg, David A Lieberman
Jun 13, 2003·Digestive and Liver Disease : Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver·A AndriulliUNKNOWN NUD LOOK Study Group
Oct 19, 2002·The American Journal of Gastroenterology·Amnon Sonnenberg

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