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.

本研究提出了一种机器学习系统，通过使用行为数字表型数据预测、检测和解释患有精神分裂症的患者症状的个体变化情况，通过使用变化点算法检测减少的症状，并在模拟持续监测的情况下使用反事实说明作为医疗保健中的纾困措施。