Automated detection of adverse events using natural language processing of discharge summaries.

By Genevieve B. Melton, George Hripcsak. JAMIA Journal of the American Medical Informatics Association, 2005

306 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a2de5c62fe3d8ad62219b04adf22987fc3cb3b50

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Citations in the corpus

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “Melton and Hripcsak [52] used MedLEE to detect adverse events deﬁned in the New York Patient Occurrence Reporting and Tracking System (NYPORTS) using discharge summaries.”
Mining Electronic Health Records: A Survey (2017, background): “…al. 2014 [Sathyanarayana et al. 2014; White et al. 2013; Iyer et al. 2014; Haerian et al. 2012; Pathak et al. 2013; Carroll et al. 2011; Xu et al. 2011a; Bobo et al. 2014; Zhang et al. 2015; Melton and Hripcsak 2005] Utilizing data mining techniques in certain application areas is very unnatural.”
Artificial Intelligence in Surgery: Promises and Perils (2018, methodology): “…been utilized for large-scale database analysis of the EMR to detect adverse events and postoperative complications from physician documentation, 17,18 and many EMR systems now incorporate NLP — for , to achieve automated claims coding — into their underlying software architecture to improve…”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “Melton and Hripcsak [135] used MedLEE to mount a surveillance for a broad range of adverse events.”
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts (2013, background): “Examples of BM-NER systems include extracting clinical information from radiology reports [1–3], identifying diseases and drug names in discharge summaries [4–6], detecting gene and protein mentions in biomedical paper abstracts [7–9].”
Automated identification of postoperative complications within an electronic medical record using natural language processing. (2011, background): “Nevertheless, there have been few studies investigating natural language processing tools for the detection of adverse events.(14,15) It is not known whether a surveillance approach based on language processing searches of free-text documents will perform better than currently used tools based on ad”
Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections (2010, methodology): “For surveillance purposes, text analyses have been applied to limited data sources within the EMR, including ‘‘chief complaint’’ fields [34,35,36,37,38], laboratory or imaging reports [34,35,38,39,40] or dictated hospital discharge summaries [41,42].”