ArticleOrphanet journal of rare diseases2018
Next generation phenotyping using narrative reports in a rare disease clinical data warehouse.
Article in Orphanet journal of rare diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 40 citations in OpenAlex.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- Improving patient clustering by incorporating structured variable label relationships in similarity measures.BMC medical research methodology · 2025Article
- Navigating disruption in the PID landscape: embracing opportunities and anticipating threats in the next ten years.Frontiers in immunology · 2025Review
- Fine-tuning large language models for rare disease concept normalization.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Fine-tuning Large Language Models for Rare Disease Concept Normalization.bioRxiv : the preprint server for biology · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Creating an automated contemporaneous cohort in sickle cell anemia to predict survival after disease-modifying therapy.Blood advances · 2023Article
- Construction of Cohorts of Similar Patients From Automatic Extraction of Medical Concepts: Phenotype Extraction Study.JMIR medical informatics · 2022Article
- Consideration of oral health in rare disease expertise centres: a retrospective study on 39 rare diseases using text mining extraction method.Orphanet journal of rare diseases · 2022Article
- The prediction of hospital length of stay using unstructured data.BMC medical informatics and decision making · 2021Observational
- Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome.Orphanet journal of rare diseases · 2021Article
- Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases.Patterns (New York, N.Y.) · 2021Article
- Generalized and transferable patient language representation for phenotyping with limited data.Journal of biomedical informatics · 2021Article
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
- Healthcare trajectory of children with rare bone disease attending pediatric emergency departments.Orphanet journal of rare diseases · 2020Article
- Different Clinical Presentations and Outcomes of Disseminated Varicella in Children With Primary and Acquired Immunodeficiencies.Frontiers in immunology · 2020Article
- A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook.Yearbook of medical informatics · 2019Article
- Clinical Research Informatics: Contributions from 2018.Yearbook of medical informatics · 2019Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSecondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children's Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest.
methodsWe leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr. Warehouse and computed the average precision and mean average precision.
resultsExperts concluded that between 16 and 39 phenotypes could be considered as relevant in the top-50 phenotypes ranked by descending frequency discovered by Dr. Warehouse (resp. between 11 and 41 for TF-IDF). Average precision ranges from 0.55 to 0.91 for frequency and 0.52 to 0.95 for TF-IDF. Mean average precision was 0.79. Our study suggests that phenotypes identified in clinical narratives stored in Electronic Health Record can provide rare disease specialists with candidate phenotypes that can be used in addition to the literature.
conclusionsClinical Data Warehouses can be used to perform Next Generation Phenotyping, especially in the context of rare diseases. We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives.
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