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

TL;DR

This paper presents a machine learning-based decision support system that predicts risk scores from vital parameters to prioritize patients in remote telemedical monitoring, improving efficiency and patient care.

Contribution

It introduces a deep learning model that outperforms baseline rule-based methods in predicting patient risk, enhancing telemedical center capacity.

Findings

Deep learning model achieves AUCROC of 0.84

Baseline rule-based model achieves AUCROC of 0.73

Model can help prioritize severe cases for better resource allocation

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.