AI-based hospitalisation detection in telemonitoring data from heart failure patients

Other text in scientific journal, 2025

Background: Heart Failure (HF) represents end-stage myocardial disease, expected to increase with an aging population. HF accounts for a
significant proportion of hospitalisations, and due to the unpredictable nature of the syndrome which often involves multiple comorbidities, can
lead to frequent readmissions post discharge. Remote patient management through telemonitoring has shown to be a promising approach for
early detection of worsening health status through continuous logging of vital parameters (VP). Implemented systems today often use
predefined thresholds to determine instances of high risk, ignoring sequential- and temporal dependencies in the data. More sophisticated
model architectures have the potential to use these data sources to solve tasks, where deep learning (DL) often outperforms more traditional
machine learning (ML) architectures by using larger and more complex networks.
Purpose: Create a DL system for detecting the deterioration of the patient's condition by estimating the probability of hospitalization in
telemonitored HF patients.
Methods: A real-world cohort of out-patients with HF undergoing telemonitoring for new or worsening HF, with daily measurements of VP (blood
pressure (BP), heart rate (HR) and weight) during 6 months, were analysed. The data sequences for each VP were filtered with clinically
relevant thresholds to remove outliers and subsequently divided into shorter time windows (length 10-14 measurements) with overlapping
boundaries. Temporal restrictions to find measurements for a window were twenty-one days. The data windows were separated into training-
and validation sets based on the patient origins, ensuring model validation on unseen information. We constructed the DL model using three
transformer-based encoders, one for each of the two BP variables and the HR, followed by task-specific secondary networks. Frozen linear
networks were used as projection heads for the anomaly-based contrastive pre-training and trainable, fully-connected linear layers were used
as the classifiers (Figure 1).
Results: We analysed n=278 patients (n=18 rehospitalisations related to HF during the monitoring period, mean age of 67.6 years, n=72
women (25.9%) 227 patients (81.7%) were classified with HFrEF). The imbalance ratio for the dataset was 222.2. The DL model was able to
correctly detect 72.3% of the rehospitalisations whilst overestimating 6.7% of non-rehospitalisations (Table 1). In comparison to a baseline ML
model, the DL model outperformed the tested XGBoost model on both sensitivity and F1-score for the unseen validation data. Furthermore, the
anomaly-based contrastive pre-training increased the ability to detect hospitalisation with 4.6%.
Conclusions: The DL model resulted in a higher performance on all evaluation metrics compared to the tested ML model. The model shows
promise in identifying adverse outcomes within a time frame that allows for counteractive treatment in patients with HF.