scholarly journals Relation Classification for Bleeding Events from Electronic Health Records: Exploration of Deep Learning Systems (Preprint)

10.2196/27527 ◽  
2021 ◽  
Author(s):  
Avijit Mitra ◽  
Bhanu Pratap Singh Rawat ◽  
David D McManus ◽  
Hong Yu
Keyword(s):  
Deep Learning ◽  
Electronic Health Records ◽  
Learning Systems ◽  
Bleeding Events ◽  
Health Records ◽  
Electronic Health ◽  
Relation Classification

scholarly journals Relation Classification for Bleeding Events from Electronic Health Records: Exploration of Deep Learning Systems (Preprint)

2021 ◽  
Author(s):  
Avijit Mitra ◽  
Bhanu Pratap Singh Rawat ◽  
David D McManus ◽  
Hong Yu
Keyword(s):  
Deep Learning ◽  
Electronic Health Records ◽  
Language Processing ◽  
Bleeding Event ◽  
Learning Systems ◽  
Bleeding Events ◽  
Convolutional Network ◽  
Health Records ◽  
Electronic Health ◽  
Relation Classification

BACKGROUND Accurate detection of bleeding events from electronic health records (EHR) 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 (e.g., bleeding anatomic sites, 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, there has been no prior work that utilized deep learning to extract relations between bleeding events and relevant entities. OBJECTIVE In this study, we aim to evaluate multiple deep learning systems on a novel EHR dataset for bleeding event related relation classification. METHODS We first expert-annotated a new dataset of 1283 de-identified EHR notes for bleeding events and their attributes. On this dataset, we evaluated three state-of-the-art deep learning architectures, namely, convolutional neural network (CNN), graph convolutional network with attention (AGGCN) and BERT-based models (BioBERT, Bio+Clinical BERT and EhrBERT) for bleeding event relation classification task. RESULTS Our experiments show that the BERT-based models significantly outperformed CNN and AGGCN. Specifically, BioBERT achieved a macro F1 score of 0.842, outperforming both AGGCN (macro F1 score, 0.828) and CNN (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 deep learning models for identifying the relations of bleeding related entities. BERT-based models were benefited from their pre-trained contextualized word representation and the use of target entity representation over traditional sequence representation.

scholarly journals Deep learning detects and visualizes bleeding events in electronic health records

2021 ◽  
Author(s):  
Jannik S. Pedersen ◽  
Martin S. Laursen ◽  
Thiusius Rajeeth Savarimuthu ◽  
Rasmus Søgaard Hansen ◽  
Anne Bryde Alnor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electronic Health Records ◽  
Bleeding Events ◽  
Health Records ◽  
Electronic Health

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records

2019 ◽  
Vol 182 ◽  
pp. 105055 ◽  
Author(s):  
Binh P. Nguyen ◽  
Hung N. Pham ◽  
Hop Tran ◽  
Nhung Nghiem ◽  
Quang H. Nguyen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

scholarly journals Readmission prediction using deep learning on electronic health records

2019 ◽  
Vol 97 ◽  
pp. 103256 ◽  
Author(s):  
Awais Ashfaq ◽  
Anita Sant’Anna ◽  
Markus Lingman ◽  
Sławomir Nowaczyk
Keyword(s):  
Deep Learning ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

scholarly journals Deep learning for electronic health records: A comparative review of multiple deep neural architectures

2020 ◽  
Vol 101 ◽  
pp. 103337 ◽  
Author(s):  
Jose Roberto Ayala Solares ◽  
Francesca Elisa Diletta Raimondi ◽  
Yajie Zhu ◽  
Fatemeh Rahimian ◽  
Dexter Canoy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electronic Health Records ◽  
Health Records ◽  
Comparative Review ◽  
Electronic Health
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