Applied Clinical InformaticsBioinformaticsPeer-reviewed journal article
Consensus Development Of A Modern Ontology Of Emergency Department Presenting Problems: The HierArchical Presenting Problem OntologY (HaPPy)
Steven HorngiD, Nathaniel R. GreenbaumiD, Larry A. Nathanson, James C. McClayiD, Foster R. GossiD, Jeffrey A. NielsoniD
Abstract
ObjectiveNumerous attempts have been made to create a standardized presenting problem or chief complaint list to characterize the nature of an Emergency Department visit. Previous attempts have failed to gain widespread adoption as none were freely sharable and contained the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges.\n\nMaterials and MethodsWe prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology; followed by a 6 month washout period, and the remaining 30% for validation. All concepts were mapped to SNOMED-CT.\n\nResultsOur system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 non-visible descriptions to correct misspellings and non-standard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation dataset.\n\nDiscussion and ConclusionWe present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED-CT. This freely sharable ontology can help to facilitate presenting problem based quality metrics, research, and patient care.
- Citations
- 20
- FWCI
- 0.74
- Field percentile
- top 33%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department
Nathaniel R. Greenbaum, Yacine Jernite, … Steven Horng·bioRxiv·Apr 12, 2017
- SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research
Honghan Wu, Giulia Toti, … Richard Dobson·Journal of the American Medical Informatics AssociationPeer-reviewed·Jan 8, 2018
- A novel tool for standardizing clinical data in a realism-based common data model
H. T. Freedman, Heather Williams, … Christian J. Stoeckert·bioRxiv·May 12, 2020
- Enterprise Health Twins - Conceptual Framework, Design Principles,and Longitudinal Evaluation: Preliminary Results from EpiToMe
Guo‐Qiang Zhang, Licong Cui, … Shiqiang Tao·bioRxiv·Jun 23, 2026
- Komenti: A semantic text mining framework
Luke T. Slater, William M. Bradlow, … Georgios V. Gkoutos·bioRxiv·Aug 4, 2020
- RDEmbed: A Sentence-Embedding Framework for Rare Disease Phenotype Representation and Phenotypic Similarity Analysis
Xinghua Wang, Jianlian Wang, … Arif Harmanci·bioRxiv·Oct 19, 2026