Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

Journals

1. Antoniou M. Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”. JMIRx Med 2021;2(3):e31550 View
2. . Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”. JMIRx Med 2021;2(3):e31548 View
3. Bright R, Rankin S, Dowdy K, Blok S, Bright S, Palmer L. Authors’ Response to Peer Reviews of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”. JMIRx Med 2021;2(3):e31568 View
4. Yu H. Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”. JMIRx Med 2021;2(3):e31551 View
5. . Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”. JMIRx Med 2021;2(3):e31547 View
6. ShojaeiBaghini M, Ghaemi M, Ahmadipour A. Artificial intelligence in the identification and prediction of adverse transfusion reactions(ATRs) and implications for clinical management: a systematic review of models and applications. BMC Medical Informatics and Decision Making 2025;25(1) View

Conference Proceedings

1. Ehghaghi M, Zhou P, Cheng W, Rajabi S, Kuo C, Lee E. 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement View