ArticleJournal of the American Medical Informatics Association : JAMIA
Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries.
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 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition.Bioinformatics (Oxford, England) · 2026Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- Machine learning and deep learning in medicine and neuroimaging.Annals of the Child Neurology Society · 2023Review
- Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study.JMIR formative research · 2022Article
- Research on Application of Data Mining Algorithm in Cardiac Medical Diagnosis System.BioMed research international · 2022Article
- Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence.Scientific reports · 2021Article
- A Deep Learning-Based Text Classification of Adverse Nursing Events.Journal of healthcare engineering · 2021Article
- ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature.BMC bioinformatics · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Automated discretization of 'transpiration restriction to increasing VPD' features from outdoors high-throughput phenotyping data.Plant methods · 2020Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records.Frontiers in medicine · 2019Article
- A computational framework for converting textual clinical diagnostic criteria into the quality data model.Journal of biomedical informatics · 2016Article
- Using local lexicalized rules to identify heart disease risk factors in clinical notes.Journal of biomedical informatics · 2015Article
- Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary.BMC bioinformatics · 2015Article
- Sieve-based relation extraction of gene regulatory networks from biological literature.BMC bioinformatics · 2015Article
- A usability framework for speech recognition technologies in clinical handover: a pre-implementation study.Journal of medical systems · 2014Article
- Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.Journal of the American Medical Informatics Association : JAMIA · 2014Article
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
No grant is acknowledged in the PubMed record.
Abstract
objectiveA system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification.
designThe overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting concepts that use a dosage-unit dictionary to dynamically switch two models based on Conditional Random Fields (CRF), (4) classifying assertions based on voting of five classifiers, and (5) identifying relations using normalized sentences with a set of effective discriminating features. MEASUREMENTS: Macro-averaged and micro-averaged precision, recall and F-measure were used to evaluate results.
resultsThe performance is competitive with the state-of-the-art systems with micro-averaged F-measure of 0.8489 for concept extraction, 0.9392 for assertion classification and 0.7326 for relation identification.
conclusionsThe system exploits an array of common features and achieves state-of-the-art performance. Prudent feature engineering sets the foundation of our systems. In concept extraction, we demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly. In assertion classification, a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible. These classes would suffer from data scarcity in conventional machine-learning methods. In relation identification, we use two-staged architecture, the second of which applies pairwise classifiers to possible candidate classes. This architecture significantly improves performance.
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.