Automated encoding of clinical documents based on natural language processing.

By Carol Friedman, L. Shagina, Y. Lussier, George Hripcsak. JAMIA Journal of the American Medical Informatics Association, 2004

553 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16f58a56302a485fe3473fa267cb67902b5b02dd

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Citations in the corpus

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support (2008, background): “Some terminologies provide mapping information to other terminologies (e.g., SNOMED to ICD-9-CM), which, in some cases is recorded in the UMLS.”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, methodology): “…safety and automated surveillance of adverse events, contraindications and epidemics [23,24,25], comorbidity and disease networks [26,27,28], autocoding of clinical text [29,30,31,32], medication information extraction [33,34] and identifying suitable individuals for clinical trials [35,36].”
Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017, unknown): “μ ∇ = − + −    g B B C A C A O C A BB S B ( ) 2 ( ) ( ) 2 4 ( ) , (14) T T B (2)”
What can Natural Language Processing do for Clinical Decision Support? (2009, methodology): “The formal representation was generated by MedLEE [68]; (4) query generation for event detection and classification; (5) verification of the accuracy of”
Artificial Intelligence in Surgery: Promises and Perils (2018, methodology): “19 In surgical patients, NLP has been used to automatically comb through EMRs to identify words and phrases in operative reports and progress notes that predicted anastomotic leak after colorectal resections.”
Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions (2013, methodology): “The clinical narratives — corresponding to discharge summaries, admission notes, and outpatient of ﬁ ce visits — were processed using the NLP system, MedLEE, 31 to extract medications, diseases, and signs and symptoms, and were mapped to UMLS (2011AA) concept unique identi ﬁ ers (CUIs).”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “In fact, numerous publicly available NLP systems have been extended to perform the ADE detection task, including MedLEE [47], MetaMap [48], cTAKES [49], MedEx [50], and GATE [51].”
Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports (2015, methodology): “Ye et al. [14] found that Topaz had an overall accuracy of 0.78 to extract findings from free-text ED reports, which was significantly better than the alternative NLP software MEDLEE [26] with an accuracy of 0.71.”
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].”
Mining electronic health records: towards better research applications and clinical care (2012, methodology): “Although still mostly used in research contexts, MedLEE has demonstrated performance similar to expert human curators 30 and is in clinical operation at several health facilities.”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Converting the unstructured data into useful structured representations will not only help data analysis but also improve efﬁciency in clinical practice (Jagan-nathan et al., 2009; Kreimeyer et al., 2017; Ford et al., 2016; Demner-Fushman et al., 2009; Murff et al., 2011; Friedman et al., 2004).”
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, methodology): “NLP is also used for automated encoding of clinical documents [16].”
Graph-based signal integration for high-throughput phenotyping (2012, methodology): “Automated coding systems such as MetaMap [13], cTAKES [14], and MedLEE [15] can map text to Unified Medical Language System (UMLS) concepts; however, without the addition of customized rules they draw no inferences from it.”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “She developed MedLEE, a natural language extraction system for the clinical field.”