Automatic classification of scanned electronic health record documents.

ABSTRACT:

Objectives

Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant categories as well as further sub-classifications. Our objective is to demonstrate that text classification systems can accurately classify scanned documents.

Methods

We extracted text using Optical Character Recognition (OCR). We then created and evaluated multiple text classification machine learning models, including both "bag of words" and deep learning approaches. We evalu

SUBMITTER: Goodrum H 

PROVIDER: S-EPMC7731898 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature