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

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.

Towards a Verification Approach of a Smart Registry for Chronic Heart Failure Patients

“…To support point-of-care and real-time patient care, an infrastructure is needed in which data from different sources can be aggregated and analyzed in realtime. [1][2][3] Based on a previous established telehealth program called HerzMobil Tirol (HMT) [4], a post-discharge disease management program for heart failure (HF) patients, a socalled "smart registry" for HF patients was created in a first step. In addition to "normal" registries, the "smart registry" is automatically synchronized with various sources, it supports pseudonymization and privacy preserving record linkage, it is standardized, and it supports personalized healthcare through smart decision support services.…”