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Cardiovascular diseases and heart failures in particular are the main causeof non-communicable disease mortality in the world. Constant patient monitoringenables better medical treatment as it allows practitioners to react on timeand provide the appropriate treatment. Telemedicine can provide constant remotemonitoring so patients can stay in their homes, only requiring medical sensingequipment and network connections. A limiting factor for telemedical centers isthe amount of patients that can be monitored simultaneously. We aim to increasethis amount by implementing a decision support system. This paper investigatesa machine learning model to estimate a risk score based on patient vitalparameters that allows sorting all cases every day to help practitioners focustheir limited capacities on the most severe cases. The model we propose reachesan AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of0.73. Our results indicate that the usage of deep learning to improve theefficiency of telemedical centers is feasible. This way more patients couldbenefit from better health-care through remote monitoring.

Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring