Predicting Clinical Outcomes with Waveform LSTMs
This work addresses the under-utilization of waveform data for clinical outcome prediction, offering incremental improvements in a specific healthcare domain.
The study tackled the problem of predicting ICU mortality risk by leveraging clinical waveform data, achieving improved prediction accuracy over existing logistic regression and deep learning baselines.
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.