Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 30; no. 3; pp. 456 - 465
• Main Authors: Wan, Nicholas C, Yaqoob, Ali A, Ong, Henry H, Zhao, Juan, Wei, Wei-Qi
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 16.02.2023
• Subjects: Algorithms; Diabetes Mellitus, Type 2; Electronic Health Records; Humans; Knowledge Bases; Phenotype; Research and Applications; online health information; electronic health records; natural language processing; high-throughput phenotyping
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocac234

Abstract Objective A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. Materials and Methods We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency—inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center’s BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement. Results Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration. Conclusions Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.