ArticleNPJ digital medicine2019
Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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.
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.
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Who cites it
39 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Toward a unified vocabulary for embodiment research challenges and solutions.NPJ science of learning · 2026Review
- Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Identifying Phenotypes for Earlier Diagnosis of Rare Diseases.Studies in health technology and informatics · 2025Article
- Hypothesizing mechanistic links between microbes and disease using knowledge graphs.Scientific reports · 2025Article
- Increased discoverability of rare disease datasets through knowledge graph integration.JAMIA open · 2025Article
- Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.bioRxiv : the preprint server for biology · 2024Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.Orphanet journal of rare diseases · 2024Article
- KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- On the limitations of large language models in clinical diagnosis.medRxiv : the preprint server for health sciences · 2024Article
- LeafAI: query generator for clinical cohort discovery rivaling a human programmer.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.JAMIA open · 2023Article
- Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.EBioMedicine · 2023Article
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
- HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.medRxiv : the preprint server for health sciences · 2022Article
- Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.Journal of medical Internet research · 2022Article
- HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
Identifiers
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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.