Causal Understanding of Patient Illness in Medical Diagnosis

Abstract

First generation AI in Medicine programs have clearly demonstrated the usefulness of AI techniques. However, it has also been recognized that the use of notions such as causal relationships, temporal patterns, and aggregate disease categories in these programs has been too weak. From our study of clinician’s behavior we realized that a diagnostic or therapeutic program must consider a case at various levels of detail to integrate overall understanding with detailed knowledge. To explore these issues, we have undertaken a study of the problem of providing expert consultation for electrolyte and acid-base disturbances. We have partly completed an implementation of ABEL, the diagnostic component of the overall effort. In this paper we concentrate on ABEL’s mechanism for describing a patient. Called the patient-specific model, this description includes data about the patient as well as the program’s hypothetical interpretations of these data in a multilevel causal network. The lowest level of this description consists of pathophysiological knowledge about the patient, which is successively aggregated into higher level concepts and relations, gradually shifting the content from pathophysiological to syndromic knowledge. The aggregate level of this description summarizes the patient data providing a global perspective for efficient exploration of the diagnostic possibilities. The pathophysiological level description provides the ability to handle complex clinical situations arising in illnesses with multiple etiologies, to evaluate the physiological validity of diagnostic possibilities being explored, and to organize large amounts of seemingly unrelated facts into coherent causal descriptions.