BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients

Abstract

Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, deep learning models have drawn increasing attention in healthcare. However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes. In this paper we introduce BERTSurv, a deep learning survival framework which applies Bidirectional Encoder Representations from Transformers (BERT) as a language representation model on unstructured clinical notes, for mortality prediction and survival analysis. We also incorporate clinical measurements in BERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality as a binary outcome (mortality prediction). With partial log-likelihood (PLL) loss, BERTSurv predicts the probability of mortality as a time-to-event outcome (survival analysis). We apply BERTSurv on Medical Information Mart for Intensive Care III (MIMIC III) trauma patient data. For mortality prediction, BERTSurv obtained an area under the curve of receiver operating characteristic curve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of multilayer perceptron (MLP) without notes. For survival analysis, BERTSurv achieved a concordance index (C-index) of 0.7. In addition, visualizations of BERT's attention heads help to extract patterns in clinical notes and improve model interpretability by showing how the model assigns weights to different inputs.

arXiv commentICDM 2021

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
• ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
• Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Cited by in corpus (2)

• SurvTRACE: Transformers for Survival Analysis with Competing Events
• Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning