Methods Inf Med 1983; 22(03): 156-166
DOI: 10.1055/s-0038-1635433
Original Artical
Schattauer GmbH

The Design and Testing of a New Approach to Computer-aided Differential Diagnosis[*]

Planung Und Prüfung Eines Neuen Verfahrens Zur Computerunterstützten Differentialdiagnose
Dana Ludwig
1   (From the Section on Medical Information Sciences, University of California, San Francisco, California)
,
D. Heilbronn
1   (From the Section on Medical Information Sciences, University of California, San Francisco, California)
› Author Affiliations
Further Information

Publication History

Publication Date:
20 February 2018 (online)

An algorithm is presented for making diagnostic inferences on the basis of a causal network model of medical knowledge. The algorithm is based on Bayes Rule, but is unique in the way that it accounts for the presence of conditional non-independence of observations and for the presence of multiple diseases in the same patient. An evaluation of the system is performed on a database of patients with chest pain. In this evaluation, the diagnostic accuracy of the system is found to be inferior to that of a logistic regression model and comparable to that of a linear discriminant function. In a review of selected cases from this database, the system can be shown to provide inferences that are not possible with other simpler statistical models. The practicality of this and other computer aids to medical diagnosis is discussed.

Es wird ein Algorithmus vorgestellt, der es erlaubt, auf der Grundlage eines kausalen Netzwerkmodells medizinischen Wissens diagnostische Schlüsse zu ziehen. Der Algorithmus basiert auf der Bayes’schen Regel, ist jedoch nicht einmalig in der Weise, daß er die Existenz bedingter Nicht-Unabhängigkeit von Beobachtungen und das Vorhandensein multipler Krankheiten beim selben Patienten begründet. Eine Bewertung des Systems wird anhand einer Datenbank von Patienten mit Brustschmerzen vorgenommen. Bei dieser Auswertung wird festgestellt, daß die diagnostische Genauigkeit des Systems derjenigen eines logistischen Regressionsmodells unterlegen und derjenigen einer linearen Diskriminanzfunktion vergleichbar ist. In einer Übersicht über ausgewählte Fälle aus dieser Datenbank kann gezeigt werden, daß das System Rückschlüsse zuläßt, die bei anderen, einfacheren statistischen Modellen nicht möglich sind. Die Praktikabilität dieser und anderer Computerhilfen für die ärztliche Diagnose wird erörtert.

* This work was supported by NIH grant LM 03376 from the National Library of Medicine.


 
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