Causal Understanding of Patient Illness in Medical Diagnosis

We have studied difficulties arising in the operations of the "first generation" of AI programs in medicine and have undertaken the development of knowledge representation structures to support needed improvements. The description of a patient in existing programs such as INTERNIST-I (Pople et al., 1975), PIP (see Chapter 6), and MYCIN (Shortliffe, 1976) starts from a single list of findings about the patient. Using a data base of associations between diseases and findings (or rules establishing those connections), these programs form an interpretation of the patient's condition that is essentially a list of possible diseases, ranked by a calculated estimate of likelihood or degree of belief in each. Researchers (Patil, 1979; Pople, 1977; Smith, 1978) have recognized the need to use notions such as causal relationships, temporal patterns, and aggregate disease categories in the description of a program's diagnostic understanding, but the mechanisms provided to do this have been too weak. For example, although causality appears as a term in descriptions in PIP and INTERNIST-I, in both cases its use is limited to guiding the propagation of likelihood measures. These programs fail to capture the human notion that explanation should rest on a chain of cause-effect deduction.