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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Acknowledgements
This work refers to the project “CardioFollow.AI: An intelligent system to improve patients’ safety and remote surveillance in follow-up for cardiothoracic surgery”, and is supported by ‘FCT - Portuguese Foundation for Science and Technology, I.P.’, with the reference DSAIPA/AI/0094/2020.
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Santos, R. et al. (2023). A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_32
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