ArticleJournal of the American Medical Informatics Association : JAMIA2019
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Article
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Economics and Equity of Large Language Models: Health Care Perspective.Journal of medical Internet research · 2024Article
- Boosting efficiency in a clinical literature surveillance system with LightGBM.PLOS digital health · 2024Article
- Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening.Cureus · 2024Article
- Article
- Multilingual RECIST classification of radiology reports using supervised learning.Frontiers in digital health · 2023Article
- A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study.JMIR medical informatics · 2021Article
- Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts.JMIR medical informatics · 2021Article
- Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.BMC medical informatics and decision making · 2021Article
- Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study.JMIR medical informatics · 2021Article
- Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer.JCO clinical cancer informatics · 2021Article
- Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods.BMC medical informatics and decision making · 2021Article
- [Artificial intelligence based Chinese clinical trials eligibility criteria classification].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Cloud-Based Federated Learning Implementation Across Medical Centers.JCO clinical cancer informatics · 2021Article
- Using natural language processing to classify social work interventions.The American journal of managed care · 2021Review
- Cohort selection for clinical trials: n2c2 2018 shared task track 1.Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveAutomated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset. MATERIALS AND
methodsWe participated in the 2018 National NLP Clinical Challenges (n2c2) Shared Task on cohort selection and received an annotated dataset with medical narratives of 202 patients for multilabel binary text classification. We set our baseline to a majority classifier, to which we compared a rule-based classifier and orthogonal machine learning strategies: support vector machines, logistic regression, and long short-term memory neural networks. We evaluated logistic regression and long short-term memory using both self-trained and pretrained BioWordVec word embeddings as input representation schemes.
resultsRule-based classifier showed the highest overall micro F1 score (0.9100), with which we finished first in the challenge. Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline. We could not show a difference in classification efficiency between self-trained and pretrained embeddings. DISCUSSION: Clinical context, negation, and value-based criteria hindered shallow machine learning approaches, while deep learning strategies could not capture the term diversity due to the small training dataset.
conclusionShallow methods for clinical phenotyping can still outperform deep learning methods in small imbalanced data, even when supported by pretrained embeddings.
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.