Detecting Adverse Drug Events with Rapidly Trained Classification Models
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DOI: 10.1007/s40264-018-0763-y
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Cited by:
- Feifan Liu & Abhyuday Jagannatha & Hong Yu, 2019. "Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records," Drug Safety, Springer, vol. 42(1), pages 95-97, January.
- Hui Xing Tan & Chun Hwee Desmond Teo & Pei San Ang & Wei Ping Celine Loke & Mun Yee Tham & Siew Har Tan & Bee Leng Sally Soh & Pei Qin Belinda Foo & Zheng Jye Ling & Wei Luen James Yip & Yixuan Tang &, 2022. "Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries," Drug Safety, Springer, vol. 45(8), pages 853-862, August.
- Abhyuday Jagannatha & Feifan Liu & Weisong Liu & Hong Yu, 2019. "Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)," Drug Safety, Springer, vol. 42(1), pages 99-111, January.
- Likeng Liang & Jifa Hu & Gang Sun & Na Hong & Ge Wu & Yuejun He & Yong Li & Tianyong Hao & Li Liu & Mengchun Gong, 2022. "Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources," Drug Safety, Springer, vol. 45(5), pages 511-519, May.
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