Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring
This work addresses the need for efficient remote patient monitoring to help practitioners prioritize severe cases in telemedicine, though it is incremental in applying deep learning to this domain.
The paper tackled the problem of limited capacity in telemedical centers by developing a machine learning model to predict medical interventions from vital parameters, achieving an AUCROC of 0.84 compared to a baseline of 0.73.
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.