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Related Experiment Video

Updated: Jun 14, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural

Icelini Stavers-Sosa1,2, David J Cronkite3, Lawrence D Gerstley4

  • 1Department of Psychiatry, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA.

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|September 2, 2024
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Summary
This summary is machine-generated.

Early identification of psychotic spectrum disorder (PSD) is vital for better outcomes. This study uses electronic health records and natural language processing to predict PSD risk in young adults, aiming to shorten the duration of untreated psychosis.

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Linguistics
  • Health Informatics

Background:

  • Timely diagnosis of psychotic spectrum disorder (PSD) is critical for effective treatment and improved patient outcomes.
  • Delayed recognition of early psychotic symptoms often leads to prolonged untreated psychosis, negatively impacting prognosis.
  • Existing diagnostic methods may not capture subtle early indicators of PSD effectively.

Purpose of the Study:

  • To develop and validate a predictive model for identifying young individuals (15-29 years) at high risk of developing a psychotic spectrum disorder.
  • To leverage electronic health record (EHR) data and natural language processing (NLP) for early PSD detection.
  • To reduce the duration of untreated psychosis by enabling earlier intervention.

Main Methods:

  • A retrospective analysis of EHR data, including clinical notes and secure messages, from patients aged 15-29 years across two Kaiser Permanente regions (2017-2019).
  • Development of a prediction model using a tri-sourced NLP feature extraction design to identify patients with new-onset PSD within 12 months of a primary care encounter.
  • Validation of the prediction model within each region and on a combined sample.

Main Results:

  • The study proposes a novel approach to PSD risk prediction using integrated EHR data and NLP.
  • The model aims to accurately identify patients with an elevated chance of developing PSD.
  • Validation strategies are in place to assess the model's performance and generalizability.

Conclusions:

  • The developed model has the potential to significantly improve early detection of psychotic spectrum disorder in young populations.
  • By leveraging comprehensive EHR data and advanced NLP techniques, this approach can facilitate timely interventions.
  • Reducing the duration of untreated psychosis through this predictive model is expected to lead to substantially better long-term patient outcomes.