ArticleJournal of biomedical informatics2023
Trends and opportunities in computable clinical phenotyping: A scoping review.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Computable phenotypes for research using real-world data: experiences from the NIH pragmatic trials collaboratory.JAMIA open · 2026Article
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms.Research square · 2026Article
- Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms.medRxiv : the preprint server for health sciences · 2026Article
- Towards an understanding of disturbed sleep phenotypes after traumatic spinal cord injury.Journal of rehabilitation medicine · 2026Observational
- Inferring high-fat dietary patterns from electronic health record data using machine learning.JAMIA open · 2026Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
- The active construction of past episodes.Translational neuroscience · 2026Article
- Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Assessing the Impact of Computable Type 2 Diabetes Phenotypes on Predicting Healthcare Utilization Using Electronic Health Records and Administrative Claims.Healthcare (Basel, Switzerland) · 2025Article
- Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models.Proceedings of machine learning research · 2025Article
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Generalizability of kidney transplant data in electronic health records - The Epic Cosmos database vs the Scientific Registry of Transplant Recipients.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Article
- Observational
- Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China.Frontiers in immunology · 2025Article
- Automated Shared Phenotype Discovery in Undiagnosed Cohorts for Rare Disease Research.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2024Article
- Multisource representation learning for pediatric knowledge extraction from electronic health records.NPJ digital medicine · 2024Article
- CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
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What Socratic 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 Socratic graph.