ArticleJournal of biomedical informatics2019
Feature extraction for phenotyping from semantic and knowledge resources.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- A methodological framework for integrating model-guided medicine and multidimensional information management systems: application in anti-aging healthcare.International journal of computer assisted radiology and surgery · 2025Article
- Automated phenotyping of congenital heart disease for dynamic patient aggregation and outcome reporting.JAMIA open · 2025Article
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study.JMIR medical informatics · 2021Article
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.Journal of medical Internet research · 2021Article
- Generative transfer learning for measuring plausibility of EHR diagnosis records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.BioData mining · 2020Article
- Automated ICD coding via unsupervised knowledge integration (UNITE).International journal of medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectivePhenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.
methodsSEmantics-Driven Feature Extraction (SEDFE) collects medical concepts from online knowledge sources as candidate features and gives them vector-form distributional semantic representations derived with neural word embedding and the Unified Medical Language System Metathesaurus. A number of features that are semantically closest and that sufficiently characterize the target phenotype are determined by a linear decomposition criterion and are selected for the final classification algorithm.
resultsSEDFE was compared with the EHR-based SAFE algorithm and domain experts on feature selection for the classification of five phenotypes including coronary artery disease, rheumatoid arthritis, Crohn's disease, ulcerative colitis, and pediatric pulmonary arterial hypertension using both supervised and unsupervised approaches. Algorithms yielded by SEDFE achieved comparable accuracy to those yielded by SAFE and expert-curated features. SEDFE is also robust to the input semantic vectors.
conclusionSEDFE attains satisfying performance in unsupervised feature selection for EHR phenotyping. Both fully automated and EHR-independent, this method promises efficiency and accuracy in developing algorithms for high-throughput phenotyping.
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.