ArticleJournal of the American Medical Informatics Association : JAMIA2015
Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Computing 3-Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes.Suicide & life-threatening behavior · 2025Article
- Clinical entity augmented retrieval for clinical information extraction.NPJ digital medicine · 2025Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing.Global spine journal · 2023Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- DES-Tcell is a knowledgebase for exploring immunology-related literature.Scientific reports · 2021Article
- A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- [Natural language processing in radiology : Neither trivial nor impossible].Der Radiologe · 2019Review
- Finding missed cases of familial hypercholesterolemia in health systems using machine learning.NPJ digital medicine · 2019Article
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
- Performing an Informatics Consult: Methods and Challenges.Journal of the American College of Radiology : JACR · 2018Article
- Scalable Electronic Phenotyping For Studying Patient Comorbidities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack.Cardiology · 2017Article
- Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data.BMC medical informatics and decision making · 2016Article
- Learning statistical models of phenotypes using noisy labeled training data.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.Journal of medical Internet research · 2016Article
- Metformin treatment status and abdominal aortic aneurysm disease progression.Journal of vascular surgery · 2016Article
- Overlap in drug-disease associations between clinical practice guidelines and drug structured product label indications.Journal of biomedical semantics · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug-drug interactions, and learning used-to-treat relationships between drugs and indications. MATERIALS: We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.
resultsThere is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.
conclusionsFor a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.
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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.