ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Automating the Transformation of Free-Text Clinical Problems into SNOMED CT Expressions.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.JMIR formative research · 2026Article
- An Automated Approach for Identifying Erroneous IS-A Relations in SNOMED CT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data.JMIR formative research · 2023Article
- The use of SNOMED CT, 2013-2020: a literature review.Journal of the American Medical Informatics Association : JAMIA · 2021Review
- A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR.Journal of biomedical informatics · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
An important function of the patient record is to effectively and concisely communicate patient problems. In many cases, these problems are represented as short textual summarizations and appear in various sections of the record including problem lists, diagnoses, and chief complaints. While free-text problem descriptions effectively capture the clinicians' intent, these unstructured representations are problematic for downstream analytics. We present an automated approach to converting free-text problem descriptions into structured Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) expressions. Our methods focus on incorporating new advances in deep learning to build formal semantic representations of summary level clinical problems from text. We evaluate our methods against current approaches as well as against a large clinical corpus. We find that our methods outperform current techniques on the important relation identification sub-task of this conversion, and highlight the challenges of applying these methods to real-world clinical text.
Identifiers
32477671PMC7233039What OpenQuestion 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 OpenQuestion graph.