Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
This work addresses mortality prediction in healthcare, but it is incremental as it builds on existing multimodal learning approaches with a specific focus on medical named entities.
The study tackled the problem of predicting in-hospital mortality risk for ICU patients by combining structured clinical data with clinical text, achieving a 2% AUC improvement over the benchmark.
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.