Statistical modeling of psychosis data.

Psychosis is a special type of mental disorder that affects around 2-3% of global population and has a strong genetic basis. Under psychosis, there is a group of diseases, which apparently may look alike and thus, it is difficult to isolate them from each other. Moreover, the credibility of real data related to psychosis is not only questionable due to its secondary nature but also its availability is grossly restricted because of the ethical constraints and prevailing social taboo. The present paper is a novel attempt to capture psychosis data by considering 24 input symptom constructs and 7 tentative responses (outputs) as per Brief Psychiatric Rating Scale-F2 (BPRS-F2). The captured input-output data as per Plackett-Burman design (PBD) of experiments (after consulting 40 psychiatrists) are statistically modeled, to determine their mutual relationships (i.e., outputs as the functions of inputs). Both Pareto-charts as well as normal probability plots are prepared to investigate the effect of each factor on different responses. Significant symptom construct(s) has/have been identified for each response. For example, emotional withdrawal has significant contribution towards schizophrenia, and so on. The psychosis data, thus collected, will be useful for further processing to extract more information of the said disease.