”PROMEDAS” : a probabilistic decision support system for medical diagnosis
The use of patient-specific Decision Support Systems (DSS) may improve the quality and efficiency of health care, while reducing its costs at the same time. The adoption of such a system is largely compatible with the principles of " Evidence Based Medicine " and patient oriented care. PROMEDAS (PRObabilistic MEdical Diagnostic Advisory System) is a prototype DSS, based on a probabilistic model and advanced computational techniques. The system offers patient specific diagnostic advice. It presents a differential diagnosis and it supports the diagnostic process by indicating the most useful next step in the diagnostic process. The system is intended to support diagnosis making in the setting of the outpatient clinic and for educational purposes. Its target-users are general internists, super specialists (i.e. endocrinologists, rheumatologists), interns and residents, medical students and others working in the hospital environment. Currently, PROMEDAS is a stand alone application. In the future PROMEDAS may be integrated with a Hospital Information System and an Electronic Patient Record. This will facilitate its use in practice, and may augment its acceptance. PROMEDAS is based on medical expert knowledge, acquired from the literature by the medical specialists in our project team. The acquired knowledge is stored in a database, in such a way that extension and maintenance of the expert knowledge is facilitated. Currently, the database covers large parts of endocrinology and lymphoma diagnostics. In near future, parts of vasculair medicine will be entered as well. From (parts of) this database, Bayesian network and an interface for PROMEDAS is automatically compiled. The network is the underlying model of PROMEDAS. Bayesian inference is used to query the system. The PROMEDAS project is funded by STW (project nr: NNN5322). The goal of the project is to demonstrate that an accurate diagnostic DSS covering a large diagnostic repertoire in internal medicine is possible. The key technical innovation is the use of advanced approximate inference methods which allow Bayesian inference to be applied to large problem instances (patent submitted). 3 4 " Promedas " , a prototype DSS