Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

Hang Lv, Zehai Chen, Yacong Yang, Shuyao Pan, Bo Xiong, Yanchao Tan, Carl Yang

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Hang LvCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.

Zehai ChenCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.

Yacong YangCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.

Shuyao PanShengli Clinical Medical College, Fujian Medical University, Fuzhou, China.

Bo XiongInstitute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany.

Yanchao TanCollege of Computer and Data Science, Fuzhou University, Fuzhou, China.

Carl YangDepartment of Computer Science, Emory University, Atlanta, GA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosis that patients may receive. However, existing studies usually ignore the fine-grained semantic and structure information in EHRs (e.g., the hierarchical relations between diseases and ICD-9 codes), which fails to provide accurate disease representation towards effective diagnosis prediction. To this end, we propose to enhance diagnosis prediction through LabCare, a framework with improved semantic and structure modeling of diseases in EHR data. LabCare can simultaneously capture rich semantic and structural relations among diseases and ICD-9 codes, which is achieved by innovatively integrating language models and box embeddings. Extensive experiments on two EHR datasets show that LabCare surpasses competitors, consistently achieving a 4.29% average improvement in Recall and NDCG metrics.

Indexed as

DiagnosisDiseaseElectronic Health RecordsNatural Language ProcessingSemanticsHumansInternational Classification of DiseasesMachine Learning

Identifiers

PMID40417544

PMCPMC12099362

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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.