Bayesian diagnostic probabilities without assuming independence of symptoms

A. Gammerman, A. R. Thatcher

Research output: Contribution to journalArticle

Abstract

The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the 'Simple Bayes' model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the 'Simple Bayes' method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalMethods of Information in Medicine
Volume30
Issue number1
Publication statusPublished - 1991

Fingerprint

Bayes Theorem
Hospital Records
Acute Pain
Abdominal Pain
Physicians

Cite this

Gammerman, A. ; Thatcher, A. R. / Bayesian diagnostic probabilities without assuming independence of symptoms. In: Methods of Information in Medicine. 1991 ; Vol. 30, No. 1. pp. 15-22.
@article{9563148ef21f4a1abe3f0a647a44bc7d,
title = "Bayesian diagnostic probabilities without assuming independence of symptoms",
abstract = "The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the 'Simple Bayes' model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the 'Simple Bayes' method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.",
author = "A. Gammerman and Thatcher, {A. R.}",
year = "1991",
language = "English",
volume = "30",
pages = "15--22",
journal = "Methods of Information in Medicine",
issn = "0026-1270",
publisher = "Schattauer GmbH",
number = "1",

}

Bayesian diagnostic probabilities without assuming independence of symptoms. / Gammerman, A.; Thatcher, A. R.

In: Methods of Information in Medicine, Vol. 30, No. 1, 1991, p. 15-22.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Bayesian diagnostic probabilities without assuming independence of symptoms

AU - Gammerman, A.

AU - Thatcher, A. R.

PY - 1991

Y1 - 1991

N2 - The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the 'Simple Bayes' model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the 'Simple Bayes' method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

AB - The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the 'Simple Bayes' model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the 'Simple Bayes' method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

UR - http://www.scopus.com/inward/record.url?scp=0026059468&partnerID=8YFLogxK

M3 - Article

VL - 30

SP - 15

EP - 22

JO - Methods of Information in Medicine

JF - Methods of Information in Medicine

SN - 0026-1270

IS - 1

ER -