ArticleArtificial intelligence in medicine2016
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
Article in Artificial intelligence in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis 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.
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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
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Introducing the TOP framework: a novel phenotyping solution for collaborative phenotype algorithm development and application.Journal of biomedical semantics · 2026Article
- Critical Appraisal of Artificial Intelligence for Rare-Event Recognition: Principles and Pharmacovigilance Case Studies.Drug safety · 2026Article
- Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.Scientific reports · 2025Article
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- Neural Networks of Knowledge: Ontologies Pioneering Precision Medicine in Neurodegenerative Diseases.Current neuropharmacology · 2025Review
- 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
- Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).Military Medical Research · 2024Article
- Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Review
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Extracellular matrix is the main targeted environment in early stage of pancreatic ductal adenocarcinoma.Gastroenterology and hepatology from bed to bench · 2023Article
- Design and Evaluation of a Computational Phenotype to Identify Patients With Metastatic Breast Cancer Within the Electronic Health Record.JCO clinical cancer informatics · 2022Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
- Identification of Incident Atrial Fibrillation From Electronic Medical Records.Journal of the American Heart Association · 2022Article
- Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users.JMIR human factors · 2022Article
- Toward a better understanding about real-world evidence.European journal of hospital pharmacy : science and practice · 2022Review
- Characterizing Patient Representations for Computational Phenotyping.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveThe combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale.
methodsBased on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review.
resultsThe practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources.
conclusionsMachine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.
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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.