CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks

• URL: http://arxiv.org/abs/2111.08585v1
• Date: Wed, 10 Nov 2021 16:53:32 GMT
• Title: CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
• Authors: Chao Pang (1), Xinzhuo Jiang (1), Krishna S Kalluri (1), Matthew Spotnitz (1), RuiJun Chen (2), Adler Perotte (1), Karthik Natarajan (1) ((1) Columbia University Irving Medical Center, (2) Geisinger)
• Abstract summary: We develop a new BERT adaptation, CEHR-BERT, to incorporate temporal information using a hybrid approach. CEHR-BERT was trained on a subset of Columbia University Irving Medical Center-York Presbyterian Hospital's clinical data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Recent studies have adapted state-of-the-art bidirectional encoder representations from transformers (BERT) architecture to structured electronic health records (EHR) data for the generation of contextualized concept embeddings, yet do not fully incorporate temporal data across multiple clinical domains. Therefore we developed a new BERT adaptation, CEHR-BERT, to incorporate temporal information using a hybrid approach by augmenting the input to BERT using artificial time tokens, incorporating time, age, and concept embeddings, and introducing a new second learning objective for visit type. CEHR-BERT was trained on a subset of Columbia University Irving Medical Center-York Presbyterian Hospital's clinical data, which includes 2.4M patients, spanning over three decades, and tested using 4-fold cross-validation on the following prediction tasks: hospitalization, death, new heart failure (HF) diagnosis, and HF readmission. Our experiments show that CEHR-BERT outperformed existing state-of-the-art clinical BERT adaptations and baseline models across all 4 prediction tasks in both ROC-AUC and PR-AUC. CEHR-BERT also demonstrated strong transfer learning capability, as our model trained on only 5% of data outperformed comparison models trained on the entire data set. Ablation studies to better understand the contribution of each time component showed incremental gains with every element, suggesting that CEHR-BERT's incorporation of artificial time tokens, time and age embeddings with concept embeddings, and the addition of the second learning objective represents a promising approach for future BERT-based clinical embeddings.

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