ArticleJournal of the American Medical Informatics Association : JAMIA
Assisted annotation of medical free text using RapTAT.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The OpenDeID corpus for patient de-identification.Scientific reports · 2021Article
- Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.BMC medical informatics and decision making · 2021Article
- Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2021Article
- Markup: A Web-Based Annotation Tool Powered by Active Learning.Frontiers in digital health · 2021Article
- Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data.JAMIA open · 2020Article
- Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.Applied clinical informatics · 2019Article
- Extraction of Ejection Fraction from Echocardiography Notes for Constructing a Cohort of Patients having Heart Failure with reduced Ejection Fraction (HFrEF).Journal of medical systems · 2018Article
- NLPReViz: an interactive tool for natural language processing on clinical text.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.JMIR medical informatics · 2017Article
- De-identification of patient notes with recurrent neural networks.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Extraction of left ventricular ejection fraction information from various types of clinical reports.Journal of biomedical informatics · 2017Article
- Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.JMIR research protocols · 2016Article
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.Yearbook of medical informatics · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectiveTo determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias. MATERIALS AND
methodsA tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment. Notes were divided into 20 batches of 19-21 documents for iterative annotation and training.
resultsThe number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ~50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85). DISCUSSION: The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias.
conclusionsPre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%.
Indexed as
Identifiers
What 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.