Layered Learning for Acute Hypotensive Episode Prediction in the ICU: An Alternative Approach
Bruno Ribeiro, Vítor Cerqueira, Ricardo Santos, Hugo Filipe Silveira Gamboa · 2021 International Conference on e-Health and Bioengineering (EHB) · 2021
Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.