An input classification scheme for use in evidence-based dynamic recurrent neuro-fuzzy prognosis

This paper presents an input classification scheme used in an evidence-based dynamic recurrent neuro-fuzzy system for prognosis in rehabilitation. All external variables which may have an effect on the outcome of the rehabilitative process are classified into facts, contexts and interventions. Their effects on patients' physical and/or physiological states, which are estimated based on available evidence, are represented by fuzzy rules and/or non-linear models of physiologic processes. The outcomes of rehabilitation are defined as functions of those states.

[1]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[2]  R. Teasell,et al.  An Evidence-Based Review of Stroke Rehabilitation , 2003, Topics in stroke rehabilitation.

[3]  D. Sackett,et al.  Evidence based medicine: what it is and what it isn't , 1996, BMJ.

[4]  Tammy Hoffmann,et al.  Evidence-based rehabilitation: A guide to practice , 2004 .

[5]  Yu Wang,et al.  An event-driven dynamic recurrent neuro-fuzzy system for adaptive prognosis in rehabilitation , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).