ArticleJournal of biomedical informatics2015
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.
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 10 papers, 2 of them syntheses that pooled it.
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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- BioHackathon 2015: Semantics of data for life sciences and reproducible research.F1000Research · 2020Article
- 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
- Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019Article
- Feature extraction for phenotyping from semantic and knowledge resources.Journal of biomedical informatics · 2019Article
- Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
- Natural Language Processing for EHR-Based Computational Phenotyping.IEEE/ACM transactions on computational biology and bioinformaticsReview
- Natural Language Processing Based Instrument for Classification of Free Text Medical Records.BioMed research internationalArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Automated phenotype identification plays a critical role in cohort selection and bioinformatics data mining. Natural Language Processing (NLP)-informed classification techniques can robustly identify phenotypes in unstructured medical notes. In this paper, we systematically assess the effect of naive, lexically normalized, and semantic feature spaces on classifier performance for obesity, atherosclerotic cardiovascular disease (CAD), hyperlipidemia, hypertension, and diabetes. We train support vector machines (SVMs) using individual feature spaces as well as combinations of these feature spaces on two small training corpora (730 and 790 documents) and a combined (1520 documents) training corpus. We assess the importance of feature spaces and training data size on SVM model performance. We show that inclusion of semantically-informed features does not statistically improve performance for these models. The addition of training data has weak effects of mixed statistical significance across disease classes suggesting larger corpora are not necessary to achieve relatively high performance with these models.
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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.