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Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps
May 6, 2024, 4:42 a.m. | Yuwei Liu, Chen Dan, Anubhav Bhatti, Bingjie Shen, Divij Gupta, Suraj Parmar, San Lee
cs.LG updates on arXiv.org arxiv.org
Abstract: Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have been advanced for early sepsis prediction, their 'black-box' nature obscures the internal logic, impairing interpretability in critical settings like ICUs. This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical …
abstract advanced arxiv attention box challenge complexity cs.ai cs.lg deep learning deep learning techniques diverse forecasting issue maps medical mortality nature prediction sepsis type units vital while
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