A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: APPLIED DATA SCIENCE AND DEMO TRACK, ECML PKDD 2023, PT VII(2023)
Abstract
Remote Patient Monitoring (RPM) in cardiac surgery can become valuable for clinicians to follow patients post-discharge closely. However, these services require additional and frequently limited human and technical resources. We present the CardioFollow.AI Framework, a decision support system to assist doctors in selecting patients to be monitored remotely. Currently supporting a clinical trial, it leverages a Machine Learning model to predict the risk of post-discharge complications. Interpretable assessments are included so that clinicians can evaluate individual predictions. Additionally, the user-friendly interface of the CardioFollow.AI Framework enhances the follow-up of discharged patients by granting access to centralised information. This paper outlines the design and implementation of the CardioFollow.AI Framework and its potential impact on improving personalised patient careq.
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Key words
Machine Learning,Decision Support Systems,Remote Patient Monitoring,Cardiothoracic Surgery
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