ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2015
A Study of Concept Extraction Across Different Types of Clinical Notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Development and validation of natural language processing pipelines to extract injury and surgery-related data elements from ACL reconstruction operative reports.BMC musculoskeletal disorders · 2026Article
- A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation.International journal of environmental research and public health · 2021Article
- Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.BMC medical informatics and decision making · 2021Article
- Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora.Frontiers in research metrics and analytics · 2021Article
- Customization scenarios for de-identification of clinical notes.BMC medical informatics and decision making · 2020Article
- Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Ensemble method-based extraction of medication and related information from clinical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.JMIR medical informatics · 2019Article
- Automatic trial eligibility surveillance based on unstructured clinical data.International journal of medical informatics · 2019Article
- Comparison of Natural Language Processing Techniques in Analysis of Sparse Clinical Data: Insulin Decline by Patients.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Entity recognition from clinical texts via recurrent neural network.BMC medical informatics and decision making · 2017Article
- Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Our research investigates methods for creating effective concept extractors for specialty clinical notes. First, we present three new "specialty area" datasets consisting of Cardiology, Neurology, and Orthopedics clinical notes manually annotated with medical concepts. We analyze the medical concepts in each dataset and compare with the widely used i2b2 2010 corpus. Second, we create several types of concept extraction models and examine the effects of training supervised learners with specialty area data versus i2b2 data. We find substantial differences in performance across the datasets, and obtain the best results for all three specialty areas by training with both i2b2 and specialty data. Third, we explore strategies to improve concept extraction on specialty notes with ensemble methods. We compare two types of ensemble methods (Voting/Stacking) and a domain adaptation model, and show that a Stacked ensemble of classifiers trained with i2b2 and specialty data yields the best performance.
Indexed as
Identifiers
26958209PMC4765588What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.