Life-threatening conditions like sepsis are a leading cause of hospital mortality. The early identification of sepsis onset allows for timely intervention aiming to save patient lives. Although showing great promise for early sepsis onset prediction, Centralized Machine Learning applications are hindered by privacy concerns. Federated Learning has the potential to counteract the mentioned limitation as it trains a global model utilizing distributed data across several hospitals without sharing the data. This research explores the potential of Federated Learning to provide a more privacy-preserving and generalizable solution for predicting sepsis onset using a Deep Federated Learning setup. Patients from the MIMIC-III dataset are classified as either septic or non-septic using relevant patient features, and sepsis onset is identified at the first hour of a detected 5-hour SIRS interval for patients diagnosed with sepsis. We compare the predictive performance of different combinations of classifiers (LSTM and GRU), patient history window lengths, prediction window lengths, and Federated Learning clients, using the metrics AUROC, AUPRC, and F1-Score. Our results show that the Centralized Machine Learning and Federated Learning setups are on par in terms of predictive performance. In addition, on average, the best-performing Federated Learning model is GRU, with a five-hour patient history window and a three-hour prediction window. Overall, the study demonstrates that the proposed Federated Learning setup can predict sepsis onset comparably to state-of-the-art centralized deep learning algorithms for varying numbers of clients, enabling healthcare institutions to collaborate on mutually beneficial tasks without sharing isolated sensitive patient information.

Sepsis poses a significant threat to public health, causing millions of deaths annually. While treatable with timely intervention, accurately identifying at-risk patients remains challenging due to the condition’s complexity. Traditional scoring systems have been utilized, but their effectiveness has waned over time. Recognizing the need for comprehensive assessment, we introduce MASICU, a novel machine learning model architecture tailored for predicting ICU sepsis mortality. MASICU is a novel multimodal, attention-based classification model that integrates interpretability within an ICU setting. Our model incorporates multiple modalities and multimodal fusion strategies and prioritizes interpretability through different attention mechanisms. By leveraging both static and temporal features, MASICU offers a holistic view of the patient’s clinical status, enhancing predictive accuracy while providing clinically relevant insights.

Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients’ mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations and can therefore not be easily shared in order to build Centralized Machine Learning models that use the combined data of multiple hospitals. Federated Learning is a Machine Learning framework designed for data privacy that can be used to circumvent this problem. In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We compare the predictive performance of Federated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score, and AUROC. Our results show that Federated Learning performs equally well as the centralized approach (for 2, 4, and 8 clients) and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we demonstrate that the prediction performance is higher when the patient history window is closer to discharge or death. Finally, we show that using the F1-score as an early stopping metric can stabilize and increase the performance of our approach for the task at hand.

Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.