Dynamic decision models for clinical diagnosis

A sequential diagnostic model for medical questioning

A medical interview is a very important part of medical treatment since it is conducted when a patient is first admitted to a hospital and treatment is decided afterwards. However, these interviews are not always carried out in sufficient detail because physicians have very heavy work-loads. The development of automated medical questioning equipment which tabulates the answers to questions into a form easily understood by physicians, which enumerates data on doubtful diseases and which indicates pertinent medical examinations may come to the aid of patients and physicians. This paper presents a new diagnostic theory for the design of automated medical questioning equipment. Diagnostic theories can be classified into batch and sequential theories; the authors have investigated the sequential one, because decisions are made using minimal data. The techniques supporting this theory are multi-class recognition systems based on independently designed dual-class recognition systems and Wald's Sequential Probability Ratio Test. To discuss the properties inherent in the present theory, classification of three pattern classes was made. These were normal, hypertension and myocardial infarction classes of patients. The mean error probability of classification was found to be 3.08%.