[Computer based teaching of diagnostic skills (as illustrated by diagnosis of pulmonary artery thromboembolism]. / Komp'iuternoe obuchenie diagnosticheskim navykam (na primere diagnostiki tromboémbolii legochnoi arterii).

AIM: To elaborate an innovating computer learning system allowing experienced doctors to share their knowledge with beginning physicians; to apply a computer expert system in teaching interns to detect pulmonary artery thromboembolism (PAT) with emphasis on PAT risk factors and symptoms, on fixation of diagnostic skills. MATERIAL AND METHODS: The latest achievements in the field of artificial intellect served the basis for design of OSTELA educational computer system which operates with decisive rules of a highly skilled cardiologist. In this system interns study PAT diagnosis without a direct contact of the teacher with a learner. The computer system and its operation are described. RESULTS: Skills of PAT diagnosis were taught to 48 interns. The number of correct answers to the control test increased by 30%, on the average. CONCLUSION: The proposed computer learning system OSTELA was successfully tried in clinics (pilot trials) and is recommended for postgraduate education of physicians.

Case-based reasoning for medical decision support tasks: the Inreca approach.

We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis and 'scale-up' to more general non-technical decision support tasks as typically given in medical domains. Inreca technology has been used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases caused by psychotropes.