Prediction of Clinical Complication Onset using Neural Point Processes
This work addresses the need for interpretable event prediction in healthcare, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of predicting adverse medical events like cardiac arrest or sepsis in critical care by applying neural temporal point processes, achieving interpretable insights across six datasets and six models.
Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can anticipate issues such as cardiac arrest, sepsis, or respiratory failure before they manifest. Recently, there has been a surge in research focusing on forecasting adverse medical event onsets prior to clinical manifestation using machine learning. However, while these models provide temporal prognostic predictions for the occurrence of a specific adverse event of interest within defined time intervals, their interpretability often remains a challenge. In this work, we explore the applicability of neural temporal point processes in the context of adverse event onset prediction, with the aim of explaining clinical pathways and providing interpretable insights. Our experiments span six state-of-the-art neural point processes and six critical care datasets, each focusing on the onset of distinct adverse events. This work represents a novel application class of neural temporal point processes in event prediction.