Predicting Clinical Outcomes with Waveform LSTMs

2025/03/13 by Michael Albada, Albada, Michael
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Sepsis Diagnosis and Treatment

paper · pdf · doi:10.48550/arxiv.2503.10925

openalex publication_date 2025/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Data mining and machine learning hold great potential to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Waveform data offers particularly detailed information on how patient health evolves over time and has the potential to significantly improve prediction accuracy on multiple benchmarks, but has been widely under-utilized, largely because of the challenges in working with these large and complex datasets. This study evaluates the potential of leveraging clinical waveform data to improve prediction accuracy on a single benchmark task: the risk of mortality in the intensive care unit. We identify significant potential from this data, beating the existing baselines for both logistic regression and deep learning models.

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