Using Machine Learning to Generate Clinical Prediction Rules for Clinical Outcomes in Schizophrenia (2017-2018)

Schizophrenia is a mental illness that affects 1.1% of the U.S. population. The disease is characterized by global deterioration in functioning and includes presence of delusions, hallucinations and cognitive deficits.

This project team tackled the problems of high frequency relapse and high economic and health system burden associated with schizophrenia. The project team laid the groundwork for development of a clinical prediction tool for use in inpatient and outpatient settings designed to help clinicians predict which patients would benefit from more intensive resources including community support or clozapine. To do so, the team applied machine learning to the Duke clinical data set that contains clinical and demographic details related to patients with schizophrenia to pinpoint the optimum predictor clinical and demographic variables.

Ultimately, this work is designed to help researchers develop a software interface wherein input of a few patient-specific demographic, illness and comorbidity variables would result in a score having prognostic implications. The prediction score could be utilized to create algorithms to facilitate appropriate advocacy for resource allocation to patients based on risk of relapse.

Timing

Summer 2017 – Spring 2018

Team Outcomes

Using Machine Learning to Predict Schizophrenia Admittance (poster by Pranav Warman, Gopalkumar Rakesh, Linda Adams, Beepul Bharti, Katherine Heller, Jane Gagliardi), presented at Duke School of Medicine Clinical Research Day, May 17, 2018

See related team, Using Machine Learning to Generate Clinical Prediction Rules for Clinical Outcomes in Schizophrenia (2018-2019).