Prediction of mental health crises based on electronic health records: probabilistic and machine learning models for clinical applications
Year: 2024
Type: dissertation
Abstract: This thesis delves into the transformative potential of Electronic Health Records and Machine Learning in the prevention of mental health crisis relapses. Our work focuses on the prediction of these relapses throughout the entire period between two consecutive mental health crises, with the aim of facilitating timely interventions to mitigate the risk of relapse and optimize healthcare resources. Concretely, we develop a personalized policy to determine the extent of time a patient needs to be closely monitored before reaching stability and a Machine Learning model that provides a regularly updated risk of relapse during the patient stability phase. In our studies, we address key challenges in transferring a Machine Learning algorithm to clinical practice. To this end, we demonstrate the successful implementation of our Machine Learning algorithm within clinical workflows, enabling preventive strategies for those patients at risk of relapse. In addition, we showed that the model can be effectively transferred across different healthcare systems. These contributions pave the way for transforming mental healthcare delivery, offering personalized care to improve patient outcomes while alleviating the burden on mental healthcare facilities.
Author: Garriga Calleja, Roger
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7060975999