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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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Taking the next step: combining incrementally valid indicators to improve recidivism prediction.

Glenn D Walters¹

¹Federal Correctional Institution, Schuylkill, PA, USA. gwalters@bop.gov

|January 29, 2011

Summary

Combining multiple indicators significantly improves recidivism prediction accuracy for federal prisoners. Both weighted and unweighted combined scores outperformed individual predictors in forecasting reoffense risk.

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Area of Science:

Criminology
Forensic Psychology
Predictive Analytics

Background:

Recidivism prediction is crucial for criminal justice.
Existing prediction models often rely on single indicators.
There is a need for improved accuracy in identifying individuals at high risk of reoffending.

Purpose of the Study:

To evaluate the effectiveness of combining multiple indicators for enhanced recidivism prediction.
To compare the predictive accuracy of combined scores versus individual indicators.

Main Methods:

A sample of released federal prisoners was randomly divided into derivation and cross-validation subsamples.
Five incrementally valid indicators from demographic, historical, adjustment, rating scale, and self-report domains were selected.
Two combined scores were calculated: an unweighted summed score and a beta-weighted summed score derived from Cox survival analysis.

Main Results:

Both the unweighted and weighted combined scores demonstrated equivalent predictive performance.
Combined scores significantly improved prediction accuracy compared to individual indicators.
Receiver operating characteristic (ROC) analyses confirmed the enhanced predictive power of combined scores.

Conclusions:

Combining multiple indicators offers a significant improvement in recidivism prediction.
Both simple summation and weighted approaches are effective for creating combined predictive scores.
The findings support the use of multi-indicator models for more accurate risk assessment in released prisoners.