DeepAI AI Chat
Log In Sign Up

Early prediction of the risk of ICU mortality with Deep Federated Learning

by   Korbinian Randl, et al.
Stockholms universitet

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 and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we show 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.


page 1

page 2

page 3

page 4


FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

Although Machine Learning (ML) can be seen as a promising tool to improv...

FedMood:Federated Learning on Mobile Health Data for Mood Detection

Depression is one of the most common mental illness problems, and the sy...

A Federated Approach for Hate Speech Detection

Hate speech detection has been the subject of high research attention, d...

Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction

In healthcare, the highest risk individuals for morbidity and mortality ...

Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data

In clinical research, the lack of events of interest often necessitates ...

Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection

Although timely sepsis diagnosis and prompt interventions in Intensive C...

Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications

The Intensive Care Unit (ICU) is a hospital department where machine lea...