Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality (2021)

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 .

## introduction survival analysis estimates the expected time until an event of interest occurs @xcite . in clinical research, it is used to understand the relationship between prognostic covariates (e.g., age and treatment) and patient survival time for important use cases such as predicting mortality of heart failure patients and providing management recommendations for intensive care units during a public health crisis like the covid-19 pandemic @xcite @xcite . clinical texts such as radiology reports contain rich information about patients that is used to diagnose disease, plan treatments and monitor progress. it also contains the high-level reasoning of human experts that requires years of knowledge accumulation and professional training @xcite . despite their clinical relevance, it is challenging to use them in survival analysis since they are largely unstructured. automatic labelers are often unable to capture detailed information to distinguish between patients, especially ones with similar conditions, because they mostly rely on a small set of manually selected labels @xcite . developing methods for accessing the critical information embedded in unstructured clinical texts holds the potential to meaningfully benefit clinical research. to bridge this gap, we propose a deep learning method to predict the survival probability of heart failure (hf) patients based on the high-dimensional feature representations of their radiology reports. concretely, we extract hidden features from the texts with bert-based @xcite models and apply a recurrent neural network (rnn) to model sequences of reports and estimate the log-risk function for the overall mortality prediction. this approach can encapsulate more textual information than hand-crafted features and incorporate higher-order temporal information from report sequences. we find that our model improves on average 5.7% in both c-index and time-dependent auc without requiring additional expert annotations. we make three contributions through this work: (1) present a novel survival analysis model to leverage feature representations from clinical texts, (2) demonstrate that our model outperforms the ones dependent on predefined expert features and that this approach can generalize across various biomedical and clinical bert models, and (3) make our work publicly available for reproduction by others. ## related work due to the lack of expert annotations, earlier automatic labelers mostly use predefined linguistic patterns to extract relevant information. negex @xcite is a regular expression algorithm that identifies observations based on specified phrases. negbio @xcite uses universal dependencies and subgraph matching in addition to regular expressions. chexpert @xcite extends negbio by adding rules to extract, classify, and aggregate mentions to improve performance. while they typically achieve a high precision, they suffer from a low recall because of their limited rules. bert @xcite is a transformerbased method that extracts feature representations of unlabeled text that are effective for transfer learning across various nlp tasks. it is adapted to a wide range of domains, including biomedical and clinical domains @xcite @xcite . recently, bert models have been applied to labeling radiology reports. chexbert @xcite and @xcite train on silverstandard datasets created with a rule-based labeler, chexpert. although they outperform rule-based labelers, these approaches need a curated training corpus which can be costly to obtain and errorprone. furthermore, their labels are still limited and can miss critical information from the reports. regarding survival analysis, the cox proportional hazards model (cph) is widely adopted as it can deal with censored data and evaluate the prognostic values of covariates simultaneously @xcite . deepsurv @xcite and deep-hit @xcite are more contemporary methods that use deep neural networks to model more complex, nonlinear relationships of predictor variables. rnn-surv @xcite and drsa (ren et al., 2019) model time-variant, sequential patterns from predictor variables. to the best of our knowledge, the compatibility of these models and high-dimensional features as covariates has not been tested. automatic extraction tools enable survival analysis to incorporate textual information from clinical texts. @xcite used a convolutional neural network to extract findings from radiology reports of heart failure patients and predict all-cause mortality with cph. @xcite performed stroke prognosis based on the document-level and sentence-level representations of mri records. our work extends this line of research by using contextual deep representations of clinical texts to perform survival analysis.