ezDI's Semantics-Enhanced Linguistic, NLP, and ML Approach for Health Informatics

Document Type

Conference Proceeding

Abstract

ezDI uses large and extensive knowledge graph to enhance linguistics, NLP and ML techniques to improve structured data extraction from millions of EMR records. It then normalizes it, and maps it with various computer-processable nomenclature such as SNOMED-CT, RxNorm, ICD-9, ICD-10, CPT, and LOINC. Furthermore, it applies advanced reasoning that exploited domain-specific and hierarchical relationships among entities in the knowledge graph to make the data actionable. These capabilities are part of its highly scalable AWS deployed heath intelligence platform that support healthcare informatics applications, including Computer Assisted Coding (CAC), Computerized Document Improvement (CDI), compliance and audit, and core measures and utilization, as well as support improved decision making that involve identification of patients at risk, patterns in diseases, outcome prediction, etc. This paper focuses on the key role of its semantic approach and techniques.

APA Citation

Goswami, R., Shah, N., & Sheth, A. P. (2015). ezDI's Semantics-Enhanced Linguistic, NLP, and ML Approach for Health Informatics. .
https://corescholar.libraries.wright.edu/knoesis/1078

Link to Record

Share

COinS