  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.
