ArticleJournal of biomedical informatics2015
The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Multifaceted Natural Language Processing Task-Based Evaluation of Bidirectional Encoder Representations From Transformers Models for Bilingual (Korean and English) Clinical Notes: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.BMC medical informatics and decision making · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.Neurospine · 2019Article
- Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge.Journal of biomedical informatics · 2017Article
- Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports.Journal of biomedical informatics · 2017Article
- Inferring the Interactions of Risk Factors from EHRs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.Journal of biomedical informatics · 2015Review
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models in conjunction with manually built lexicons to classify triggers specific to each risk factor. The features used for classification were quite simple, utilizing only lexical information and ignoring higher-level linguistic information such as syntax and semantics. Instead, we incorporated high-quality data to train the models by annotating additional information on top of a standard corpus. Despite the relative simplicity of the system, it achieves the highest scores (micro- and macro-F1, and micro- and macro-recall) out of the 20 participants in the 2014 i2b2/UTHealth Shared Task. This system obtains a micro- (macro-) precision of 0.8951 (0.8965), recall of 0.9625 (0.9611), and F1-measure of 0.9276 (0.9277). Additionally, we perform a series of experiments to assess the value of the annotated data we created. These experiments show how manually-labeled negative annotations can improve information extraction performance, demonstrating the importance of high-quality, fine-grained natural language annotations.
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.