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Supporting Students With Electronic Health Record–Embedded Learning Aids: A Mixed-Methods Study

Sanne Peters, Geraldine Clarebout, Bert Aertgeerts, Jimmie Leppink, Ann Roex

JMIR Med Educ 2019;5(1):e11351

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Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis

Nicolas Delvaux, Bert Vaes, Bert Aertgeerts, Stijn Van de Velde, Robert Vander Stichele, Peter Nyberg, Mieke Vermandere

JMIR Form Res 2020;4(10):e16094

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Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation

Nicolas Delvaux, Bert Aertgeerts, Johan CH van Bussel, Geert Goderis, Bert Vaes, Mieke Vermandere

JMIR Med Inform 2018;6(4):e11428

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Human Computation as a New Method for Evidence-Based Knowledge Transfer in Web-Based Guideline Development Groups: Proof of Concept Randomized Controlled Trial

Annemie Heselmans, Bert Aertgeerts, Peter Donceel, Stijn Van de Velde, Peter Vanbrabant, Dirk Ramaekers

J Med Internet Res 2013;15(1):e8

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EBMPracticeNet: A Bilingual National Electronic Point-Of-Care Project for Retrieval of Evidence-Based Clinical Guideline Information and Decision Support

Stijn Van de Velde, Robert Vander Stichele, Benjamin Fauquert, Siegfried Geens, Annemie Heselmans, Dirk Ramaekers, Ilkka Kunnamo, Bert Aertgeerts

JMIR Res Protoc 2013;2(2):e23

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Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

We denoted the BERT-based NER model as BERT-ner, and the BERT-based family member attributes (ie, family role, side of family, negation, living status) classification module as BERT-cls and relation extraction module as BERT-rel.Figure 3 illustrates the fine-tuning

Xi Yang, Hansi Zhang, Xing He, Jiang Bian, Yonghui Wu

JMIR Med Inform 2020;8(12):e22982

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Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

Here, we further investigated three deep-learning methods with pretrained language representation models, BERT, robustly optimized BERT pretraining approach (RoBERTa) [18], and generalized autoregressive pretraining for language understanding (XLNET) [19],

Xiaofeng Wang, Shuai Chen, Tao Li, Wanting Li, Yejie Zhou, Jie Zheng, Qingcai Chen, Jun Yan, Buzhou Tang

JMIR Med Inform 2020;8(7):e17958

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Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models

We explored state-of-the-art transformer-based models (BERT, XLNet, and RoBERTa) for clinical STS.

Xi Yang, Xing He, Hansi Zhang, Yinghan Ma, Jiang Bian, Yonghui Wu

JMIR Med Inform 2020;8(11):e19735

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Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma

The BERT–CRF model [13] and multilingual BERT model [14] were trained on different languages such as Portuguese and the F1 score was ultimately improved. Today, the BERT model has also been applied in biomedical research.

Hongyu Kang, Jiao Li, Meng Wu, Liu Shen, Li Hou

JMIR Med Inform 2020;8(10):e20291

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Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

The system is based on bidirectional encoder representation from transformers (BERT) [18] and includes the 2 other types of representations besides BERT: (1) character-level representation to tackle the out-of-vocabulary (OOV) problem in natural language processing

Ying Xiong, Shuai Chen, Qingcai Chen, Jun Yan, Buzhou Tang

JMIR Med Inform 2020;8(12):e23357

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