title: Automated medical diagnosis with fuzzy stochastic models: monitoring chronic diseases.
abstract: As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people are usually monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling the population of healthy patients autonomously and by drawing the physicians' attention to the patients-at-risk, the system allows physicians to spend comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We have based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficial to the patients' health.
authors.affiliation: LORIA, MAIA INRIA Team, Campus Scientifique, 54506 Vandoeuvre-lès-Nancy, France. laurent.jeanpierre@loria.fr
authors.forename: Laurent
authors.initials: L
authors.lastname: Jeanpierre
authors.forename: François
authors.initials: F
authors.lastname: Charpillet
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007676:Kidney Failure, Chronic / Q000175:diagnosis / Q000503:physiopathology* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D010531:Peritoneal Dialysis, Continuous Ambulatory; D013269:Stochastic Processes