Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress

In the medical literature there are a number of physiological reactions related to cognitive activities. Psychosocial and psychophysiological stress is such activities reflected in physiological reactions. Stress related symptoms are highly individual, but decreased hands temperature is the common for most individuals. A clinician learns with experience how to interpret the different symptoms but there is no adaptive diagnostic system for diagnosing stress. Decision support systems (DSS) for stress diagnosis would be valuable both for junior clinicians and as second opinion for experts. Due to the large individual variations and no general set of rules, DSS are difficult to build for this task. The proposed solution combines a calibration phase with case-based reasoning approach and fuzzification of cases. During the calibration phase a number of individual parameters and case specific fuzzy membership functions are established. This case-based approach may help the clinician to make a diagnosis, classification and treatment plan. The case may also be used to follow the treatment progress. This may be done using the proposed system. Initial tests show promising results. The individual cases including calibration and fuzzy membership functions may also be used in an autonomous system in home environment for treatment programs for individuals often under high stress.