Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study

Abstract

Background Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities. Objective In this study, we aimed to evaluate multiple DL systems on a novel EHR data set for bleeding event–related relation classification. Methods We first expert annotated a new data set of 1046 deidentified EHR notes for bleeding events and their attributes. On this data set, we evaluated three state-of-the-art DL architectures for the bleeding event relation classification task, namely, convolutional neural network (CNN), attention-guided graph convolutional network (AGGCN), and Bidirectional Encoder Representations from Transformers (BERT). We used three BERT-based models, namely, BERT pretrained on biomedical data (BioBERT), BioBERT pretrained on clinical text (Bio+Clinical BERT), and BioBERT pretrained on EHR notes (EhrBERT). Results Our experiments showed that the BERT-based models significantly outperformed the CNN and AGGCN models. Specifically, BioBERT achieved a macro F1 score of 0.842, outperforming both the AGGCN (macro F1 score, 0.828) and CNN models (macro F1 score, 0.763) by 1.4% (P<.001) and 7.9% (P<.001), respectively. Conclusions In this comprehensive study, we explored and compared different DL systems to classify relations between bleeding events and other medical concepts. On our corpus, BERT-based models outperformed other DL models for identifying the relations of bleeding-related entities. In addition to pretrained contextualized word representation, BERT-based models benefited from the use of target entity representation over traditional sequence representation

Precision information extraction for rare disease epidemiology at scale

“…Recently, multiple deep learning approaches to NLP have used Bidirectional Encoder Representations from Transformers[ 39 ] (BERT)[ 40 ] with self-supervised pre-training on PubMed and PubMed Central and fine-tuned them in a fully-supervised manner to achieve state-of-the-art performance on several biomedical named entity recognition (NER) [ 41 , 42 ] and entity normalization tasks [ 43 ]. In the related clinical domain, pre-training on clinical notes [ 44 – 46 ] and fine-tuning on electronic health records [ 47 ] have been demonstrated to identify semantically similar sentences for note summarization [ 48 ], classify relations between bleeding events and clinical entities for better detection of bleeding [ 49 ], perform clinical entity normalization [ 47 ], and predict diseases [ 50 ]. Based on the bidirectional transformer’s ability to transfer deep contextual learning and its recent success on a wide variety of NLP tasks, we hypothesize that a BioBERT-based model will be effective for EI extraction [ 41 ], particularly given that rare diseases have less training data available.…”

Using Data Science to Improve Outcomes for Persons with Opioid use Disorder

“…Mobile devices are also a good option for deploying deep learning and reinforcement learning models. Deep learning has shown to improve a broad range of clinical applications such as detection of drug discontinuation events, improved phenotyping, making diagnoses based on clinical images, and prediction of clinical outcomes 46 , 67 – 74 . In reinforcement learning, the computer uses real-time trial and error in an interactive environment to maximize the total cumulative reward (e.g., maximize the probability of obtaining the intended outcome).…”

BertSRC: transformer-based semantic relation classification

“…The current leading NLP models such as BERT [20], GPT [21], and T5 [22] announced later are all based on this transformer block. In particular, BERT is commonly used in biomedical text mining research because it is built on multiple transformers encoder blocks, which has the advantage of compressing the sentence and mining semantic information from it [8,[23][24][25].…”

“…BERT is also being utilized in the medical and clinical fields to automatically analyze various medical data such as electronic health records [ 24 , 28 ]. The need for a systematic review is emerging for evidence-based diagnosis and treatment in the medical field.…”