Automated grouping of medical codes via multiview banded spectral clustering
| Autor: | Yuri Ahuja, Tianrun Cai, Joshua C. Denny, Robert J. Carroll, Luwan Zhang, Isaac S. Kohane, Yuk-Lam Ho, Andrew L. Beam, Katherine P. Liao, Yichi Zhang, Kelly Cho, Tianxi Cai, Zeling He |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Rand index Health Informatics Medical classification computer.software_genre Article Automation 03 medical and health sciences 0302 clinical medicine Knowledge extraction International Classification of Diseases Cluster Analysis Electronic Health Records Humans 030212 general & internal medicine 030304 developmental biology Interpretability 0303 health sciences Dimensionality reduction Spectral clustering Computer Science Applications Information extraction Data mining computer Algorithms Data integration |
| Zdroj: | J Biomed Inform |
| ISSN: | 1532-0464 |
| DOI: | 10.1016/j.jbi.2019.103322 |
| Popis: | Objective With its increasingly widespread adoption, electronic health records (EHR) have enabled phenotypic information extraction at an unprecedented granularity and scale. However, often a medical concept (e.g. diagnosis, prescription, symptom) is described in various synonyms across different EHR systems, hindering data integration for signal enhancement and complicating dimensionality reduction for knowledge discovery. Despite existing ontologies and hierarchies, tremendous human effort is needed for curation and maintenance – a process that is both unscalable and susceptible to subjective biases. This paper aims to develop a data-driven approach to automate grouping medical terms into clinically relevant concepts by combining multiple up-to-date data sources in an unbiased manner. Methods We present a novel data-driven grouping approach – multi-view banded spectral clustering (mvBSC) combining summary data from multiple healthcare systems. The proposed method consists of a banding step that leverages the prior knowledge from the existing coding hierarchy, and a combining step that performs spectral clustering on an optimally weighted matrix. Results We apply the proposed method to group ICD-9 and ICD-10-CM codes together by integrating data from two healthcare systems. We show grouping results and hierarchies for 13 representative disease categories. Individual grouping qualities were evaluated using normalized mutual information, adjusted Rand index, and F1-measure, and were found to consistently exhibit great similarity to the existing manual grouping counterpart. The resulting ICD groupings also enjoy comparable interpretability and are well aligned with the current ICD hierarchy. Conclusion The proposed approach, by systematically leveraging multiple data sources, is able to overcome bias while maximizing consensus to achieve generalizability. It has the advantage of being efficient, scalable, and adaptive to the evolving human knowledge reflected in the data, showing a significant step toward automating medical knowledge integration. |
| Databáze: | OpenAIRE |
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