Abstract
Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring .
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Acknowledgment
We thank Prof. Dr. med. Friedrich Köhler and his team for the provisioning of the dataset and Prof. Dr. med. Alexander Meyer for helping and supporting us in analyzing the dataset. We are grateful to Alexander Acker for the fruitful discussions, Volker Möller for his help with the dataset, and Boris Pfahringer for his valuable feedback on our evaluation. This research and the Telemed5000 project have been supported by the Federal Ministry for Economic Affairs and Energy of Germany as part of the program “Smart Data” (project number 01MD19014C).
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Gontarska, K., Wrazen, W., Beilharz, J., Schmid, R., Thamsen, L., Polze, A. (2021). Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_33
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DOI: https://doi.org/10.1007/978-3-030-77211-6_33
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