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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Statistical modeling of psychosis data.

S Chattopadhyay1, D K Pratihar, S C De Sarkar

  • 1School of Information Technology, Indian Institute of Technology, Kharagpur, India. subhagatachatterjee@yahoo.com

Computer Methods and Programs in Biomedicine
|May 14, 2010
PubMed
Summary

This study models psychosis symptoms using 24 constructs and 7 responses from the Brief Psychiatric Rating Scale-F2 (BPRS-F2). It identifies key symptom relationships to better understand and differentiate psychosis disorders.

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Area of Science:

  • Psychiatry
  • Computational Psychiatry
  • Mental Health Research

Background:

  • Psychosis affects 2-3% of the global population and has a strong genetic component.
  • Differentiating between various psychosis disorders is challenging due to symptom overlap.
  • Limited and questionable psychosis data availability is hindered by ethical and social factors.

Purpose of the Study:

  • To develop a novel method for capturing and statistically modeling psychosis data.
  • To identify relationships between specific symptom constructs and diagnostic outputs.
  • To facilitate further data processing for enhanced understanding of psychosis.

Main Methods:

  • Utilized 24 input symptom constructs and 7 tentative responses based on the Brief Psychiatric Rating Scale-F2 (BPRS-F2).
  • Employed Plackett-Burman design (PBD) for data capture, informed by consultations with 40 psychiatrists.
  • Applied statistical modeling, including Pareto charts and normal probability plots, to analyze input-output relationships.

Main Results:

  • Statistically modeled the relationships between input symptom constructs and output responses.
  • Identified significant symptom constructs contributing to specific psychosis responses.
  • Demonstrated that emotional withdrawal is a significant factor in schizophrenia, among other findings.

Conclusions:

  • The developed data capture and modeling approach provides a robust framework for psychosis research.
  • Significant symptom-response relationships were identified, aiding in the differentiation of psychosis disorders.
  • The collected psychosis data serves as a valuable resource for future computational and clinical investigations.