Detecting Adverse Drug Events with Rapidly Trained Classification Models.
Introduction: Identifying occurrences of medication side effects and adverse drug events (ADEs) is an important and challenging task because they are frequently only mentioned in clinical narrative and are not formally reported.Methods: We developed a natural language processing (NLP) system that ai...
| Publicado en: | Drug Safety Vol. 42; no. 1; pp. 147 - 157 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134584694&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134584694 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01145916 C5E jtl: Drug Safety issn: 01145916 maglogo: N pubinfo: dt: Jan2019 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134584694 134584694 NLM30649737 134584694 10.1007/s40264-018-0763-y NLM30649737 134584694 ppf: 147 ppct: 10 formats: tig: atl: Detecting Adverse Drug Events with Rapidly Trained Classification Models. aug: au: Chapman, Alec B. Peterson, Kelly S. Alba, Patrick R. DuVall, Scott L. Patterson, Olga V. affil: Health Fidelity, San Mateo, CA, USA sug: subj: Adverse Drug Event Trends Adverse Drug Event Epidemiology Natural Language Processing Human Adverse Drug Event Adverse Drug Event Standards Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Scales ab: Introduction: Identifying occurrences of medication side effects and adverse drug events (ADEs) is an important and challenging task because they are frequently only mentioned in clinical narrative and are not formally reported.Methods: We developed a natural language processing (NLP) system that aims to identify mentions of symptoms and drugs in clinical notes and label the relationship between the mentions as indications or ADEs. The system leverages an existing word embeddings model with induced word clusters for dimensionality reduction. It employs a conditional random field (CRF) model for named entity recognition (NER) and a random forest model for relation extraction (RE).Results: Final performance of each model was evaluated separately and then combined on a manually annotated evaluation set. The micro-averaged F1 score was 80.9% for NER, 88.1% for RE, and 61.2% for the integrated systems. Outputs from our systems were submitted to the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) competition (Yu et al. in http://bio-nlp.org/index.php/projects/39-nlp-challenges , 2018). System performance was evaluated in three tasks (NER, RE, and complete system) with multiple teams submitting output from their systems for each task. Our RE system placed first in Task 2 of the challenge and our integrated system achieved third place in Task 3.Conclusion: Adding to the growing number of publications that utilize NLP to detect occurrences of ADEs, our study illustrates the benefits of employing innovative feature engineering. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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