Using Machine Learning to Generate Clinical Prediction Rules for Clinical Outcomes in Schizophrenia (2018-2019)

The worldwide economic burden associated with caring for patients with schizophrenia has doubled in the last 10 years, from $62.7 billion in 2002 to $155.7 billion in recent years. Direct healthcare costs (inpatient care, emergency visits, medication costs and long-term care) account for 22-24% of all healthcare expenditures. Patients with schizophrenia are high utilizers of emergency department (ED) services because of relapse, which may be caused by psychoactive substance use, not taking medications as prescribed and/or lack of efficacy of interventions. These patients frequently need inpatient care, but insufficient resources lead to a situation in which patients are often kept in the ED until the crisis resolves, the substances dissipate from their system, interventions take effect or an inpatient bed becomes available.

This Bass Connections project fostered effective allocation of resources by assessing relapse risk and applying community supports and priority inpatient beds according to risk. The project team used machine learning in order to start developing a clinical prediction tool to predict the risk of relapse and prognosis of schizophrenia for every patient diagnosed with the condition. Based on predicted risk of relapse, resource allocation can be optimized to target reduced relapse rate and, ultimately, result in less frequent visits to the ED.

The 2017-18 team extracted clinical data on 1,350 patients with schizophrenia from Duke electronic health records along with all provider notes. Data in these notes added features for the team’s machine learning prediction tool, which is currently modeled based on diagnosis, medications, problem lists, insurance and other demographics. The 2018-19s team continued researching schizophrenia and machine learning algorithms. They began preliminary modeling and plan to continue working on some feature engineering and rerunning models in the upcoming year. 

Timing

Summer 2018 – Fall 2019  

Team Outputs

Machine Learning on Electronic Health Record Data in Schizophrenia (poster by Kamyar Yazdani, Abhi Jadhav, Sanya Kochhar, Aakash Thumaty, Pranav Warman, Sam Lusk, Xue Zou, Dylan Qi Liu, Myung Woo, Colette Blach, Jane P. Gagliardi, Jessica D. Tenenbaum, presented at Bass Connections Showcase, Duke University, April 17, 2019)

Machine Learning on Structured EHR Data for Prediction in Schizophrenia: Feature Engineering and Pipeline Construction (poster by Kamyar Yazdanhi, Abhi Jadhav, Aakash Thumaty, Sanya Kochhar, Pranav Warman, Jane P. Gagliardi, Jessica Tenenbaum, presented at Duke Research Computing Symposium, Duke University, January 16, 2019)

Preliminary Findings in Natural Language Processing to Stratify Patients with Mental Illness (poster by Myung Woo, Dylan Liu, Stephen Evans, Jane P. Gagliardi, Jessica Tenenbaum, presented at Duke Research Computing Symposium, Duke University, January 16, 2019)

This Team in the News

Meet the 2019 Recipients of Bass Connections Student Research Awards

See earlier related team, Using Machine Learning to Generate Clinical Prediction Rules for Clinical Outcomes in Schizophrenia (2017-2018).

Image of brains.

Team Leaders

  • Jane Gagliardi, School of Medicine-Psychiatry and Behavioral Sciences
  • Gopalkumar Rakesh, School of Medicine-Psychiatry and Behavioral Sciences
  • Jessica Tenenbaum, School of Medicine-Biostatistics and Bioinformatics

/graduate Team Members

  • Qi Liu, Statistical Science - MS
  • Casey Riffel, Masters of Public Policy
  • Allison Young, Interdisciplinary Data Science - Masters
  • Xue Zou, Comp Biology and Bioinfo-PHD

/undergraduate Team Members

  • Abhishek Jadhav, Biomedical Engineering (BSE), Computer Science (BS2)
  • Sanya Kochhar, Computer Science (BS)
  • Aakash Thumaty, Computer Science (BS), History (AB2)
  • Pranav Warman, Computer Science (BS), Biology (BS2)
  • Kamyar Yazdani, Biology (BS), Computer Science (AB2)

/yfaculty/staff Team Members

  • Myung Woo, School of Medicine-Medicine: General Internal Medicine