Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality

• URL: http://arxiv.org/abs/2105.01009v1
• Date: Mon, 3 May 2021 16:54:52 GMT
• Title: Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality
• Authors: Hyun Gi Lee, Evan Sholle, Ashley Beecy, Subhi Al'Aref and Yifan Peng
• Abstract summary: We present a novel method of using BERT-based hidden layer representations of clinical texts to predict patient survival outcomes. We show that hidden layers yield notably more accurate predictions than predefined features, outperforming the previous baseline model by 5.7% on average across C-index and time-dependent AUC.
• Score: 10.075717786962896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utilizing clinical texts in survival analysis is difficult because they are largely unstructured. Current automatic extraction models fail to capture textual information comprehensively since their labels are limited in scope. Furthermore, they typically require a large amount of data and high-quality expert annotations for training. In this work, we present a novel method of using BERT-based hidden layer representations of clinical texts as covariates for proportional hazards models to predict patient survival outcomes. We show that hidden layers yield notably more accurate predictions than predefined features, outperforming the previous baseline model by 5.7% on average across C-index and time-dependent AUC. We make our work publicly available at https://github.com/bionlplab/heart_failure_mortality.

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