(T) An interesting research from Google Health on predicting organ dysfunctions in intensive care units (ICUs). The system uses multi-task models, which take into account a variety of competing risks along with the interdependencies between the organ systems that factor into the patient outcomes in the ICU.
Abstract of the paper:
“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...
The task given to the model was to predict the onset of a selection of adverse events within 24–48 hours for every hour after a patient’s admission into the ICU. The defined adverse events included acute kidney injury (AKI), continuous renal replacement therapy (CRRT) dialysis, administration of vasopressors and inotropes, mechanical ventilation (MV), mortality, and remaining length of stay (LoS).“
Following is the blog article “Multi-task Prediction of Organ Dysfunction in ICUs” ,and the published paper “Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing“.
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