Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

ABSTRACT:

Background

Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures.

Methods

Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models

SUBMITTER: Burns ML 

PROVIDER: S-EPMC7665375 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature