Work

Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing

Year: 2019

Type: book-chapter

Abstract: Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.

Source: Studies in health technology and informatics

DOI: 10.3233/shti190482

Authors: Dorr David, Bejan Cosmin A., Pizzimenti Christie, Singh Sumeet, Storer Matt, Quinones Ana

Institutions: Oregon Health & Science University, Vanderbilt University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W2972879529