Developing Natural Language Processing Algorithms to Medically Code Clinical Notes in Electronic Health Records

Abstract

Coding medical data involves analyzing unstructured text fields, including lengthy clinical narratives, physician and nurse notes, lab reports, discharge summaries, scanned documents, and medications. Unstructured text data typically requires extensive resources to annotate, describe, analyze, and convert into meaningful and actionable information. These clinical data are often found in the Subjective, Objective, Assessment, and Plan (SOAP) notes. This presentation will demonstrate and discuss various machine learning algorithms using natural language processing (NLP) to parse SOAP notes that reside in the Theater Medical Data Store (TMDS)—specifically to estimate the injury subcategories (e.g., fractures of the lower limb), the three-digit International Classification of Diseases, 9th revision (ICD-9) code (e.g., 824, fracture of ankle), or the principal ICD-9-Clinical Modification code (e.g., 824.1, fracture of medial malleolus, open). The machine learning algorithms will be trained using manually coded diagnostic data obtained from the Expeditionary Medical Encounter Database and compared to the TMDS for accuracy. Sensitivity and accuracy will be the primary performance metrics that determine model efficiency. This research project’s long-term objective is to transition the translated coded data into a clinical encounter repository, which then can be used in conjunction with Department of Defense medical data repositories. Disclaimer: I am a military service member or employee of the U.S. Government. This work was prepared as part of my official duties. Title 17, U.S.C. §105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. §101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. This work was supported by Defense Medical Research and Development Program under work unit no. N1214. The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable Federal regulations governing the protection of human subjects. Research data were derived from an approved Naval Health Research Center, Institutional Review Board protocol number NHRC.2003.0025.