Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping
Name
cfe-2023.pdf
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1.06 MB
Format
Adobe PDF
Checksum (MD5)
035778678bc5fce765704674f2d2bd00
Author(s)
Canas, Juan Sebastian
Gomez, Francisco
Costilla Reyes, Omar
Date Issued
June 15, 2023
Abstract
Clinical practice in psychiatry is burdened with the increased demand for healthcare services and the scarce resources available. New paradigms of health data powered with machine learning techniques could open the possibility to improve clinical workflow in critical stages of clinical assessment and treatment in psychiatry.
In this work, we propose a machine learning system capable of predicting, detecting, and explaining individual changes in symptoms of patients with Schizophrenia by using behavioral digital phenotyping data. We forecast symptoms of patients with an error rate below 10%.
The system detects decreases in symptoms using changepoint algorithms and uses counterfactual explanations as a recourse in a simulated continuous monitoring scenario in healthcare. Overall, this study offers valuable insights into the performance and potential of counterfactual explanations, predictive models, and change-point detection within a simulated clinical workflow. These findings lay the foundation for further research to explore additional facets of the workflow, aiming to enhance its effectiveness and applicability in real-world healthcare settings. By leveraging these components, the goal is to develop an actionable, interpretable, and trustworthy integrative decision support system that combines real-time clinical assessments with sensor-based inputs.
Subjects
conterfactual
machine learning
digital phenotyping
interpretability
mental health
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