Prediction of Clinical Complication Onset using Neural Point Processes

TL;DR

This paper investigates neural temporal point processes for predicting the onset of critical adverse medical events, aiming to improve interpretability and clinical pathway understanding in intensive care settings.

Contribution

It introduces the novel application of neural temporal point processes to predict adverse medical events, enhancing interpretability over existing models.

Findings

Evaluated six neural point process models across six critical care datasets.

Demonstrated improved interpretability in predicting adverse event onsets.

Showed potential for clinical pathway explanation and decision support.

Abstract

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.