Pattern Recognition to Automate Chronic Patients Follow-Up and to Assist Outpatient Diagnostics

Abstract

Pattern Recognition detects templates in images and signals of all sorts, including what comes from sensors, communications, and information management devices. The Internet of Things is increasingly available in the population, coincidentally with soaring proportions of help-dependent people. Engineering development, in addition to consumer products and industrial goods, is offering a growing number and variety of home care and personal devices. Considering personal assistants will be in great shortage in the very near future, automatic detection of problems – both environmental and medical – is a must. The shortage of qualified helpers at home – eventually and partially substituted by automatic systems – calls for psychological adaptation and cultural changes. Once restricted to small groups of well-cared-for citizens, the benefits of medicine in the twenty-first century can be shared by ever-larger numbers of individuals, thanks to technology dissemination and Pattern Recognition. In addition to magic, placebo, and personal interaction with a physician, people of the present century will be able to rely upon technology to live longer lives. A system is first described – SIMIC – to help perform cardiac failure patient follow-up for elderly and chronic condition persons. Secondly, a personal learned assistant for physicians – PRAXIS – is described, giving him or her the support of all previous cases treated, organized in a knowledge base to reduce diagnostic errors and to increase diagnostics as well as patient management efficiency.