Multi-task Prediction of Organ Dysfunction in ICUs

Posted by Subhrajit Roy, Research Scientist and Diana Mincu, Research Software Engineer, Google Research

The intensive care unit (ICU) of a hospital looks after the most medically vulnerable patients, many of whom require organ support, such as mechanical ventilation or dialysis. While always critical, the demand on ICU services during the COVID-19 pandemic has further underscored the importance of data-driven decision-making in healthcare. Furthermore, the ability to accurately predict the clinical outcomes of ICU patients has the potential to guide therapy and may inform decisions about most effective care, including staffing and triage support.

Applying machine learning (ML) to electronic health records (EHRs) has shown promise in predicting clinical outcomes. However, many of these ML models are based on single-task learning (ST), where the models are trained only to predict a specific adverse event, such as an organ dysfunction or the need for a life-support intervention. Of greater benefit would be to train multi-task models, which take into account a variety of competing risks along with the interdependencies between organ systems that factor into patient outcomes in a realistic setting.

In “Multi-task prediction of organ dysfunction in the ICU using sequential sub-network routing”, we propose a multi-task learning (MTL) architecture, called Sequential Sub-Network Routing (SeqSNR), that better captures the complexity of a realistic setting. Inspired by a clinician’s holistic approach to diagnosing problems, SeqSNR is designed to use flexible parameter sharing and routing to find related tasks and encourage cross-learning between them. We successfully applied SeqSNR to the task of continuous adverse event prediction in an ICU setting and showed advantages over single-task and naïve multi-tasking, especially in low training data scenarios.

Data and Labels
In this study, we used the freely available, open access, de-identified MIMIC-III EHR dataset, which includes a patient cohort consisting of 36,498 adults across 52,038 critical care admissions at the Beth Israel Deaconess Medical Center between

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