ABSTRACT
OBJECTIVE: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. MATERIALS AND METHODS: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. RESULTS: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). CONCLUSION: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
Subject(s)
Electronic Health Records , Natural Language Processing , Data Management , Humans , Machine Learning , Social Determinants of HealthABSTRACT
The behavioral variant of frontotemporal dementia is usually a sporadic and progressive neurodegenerative disorder. Here, we report the subacute onset of a frontotemporal dementia phenotype with a treatable etiology. The patient has a history of rheumatoid arthritis, episcleritis, and thyroid eye disease on immunosuppressive therapy. He experienced a rapid personality change, including inappropriate behavior, which suggested frontotemporal dementia. Results of imaging and neuropsychological testing also suggested frontotemporal dementia. Because of his autoimmune diseases and unusually short onset of symptoms, serum paraneoplastic panel and cerebrospinal fluid were analyzed and revealed elevated P/Q- and N-type calcium channel antibodies. Treatment with therapeutic plasma exchange resulted in a rapid improvement of his behavior and cognition. This case suggests that there may be some treatable causes of frontotemporal dementia symptomatology, that is, paraneoplastic antibodies. In the context of atypical features of frontotemporal dementia, practitioners should maintain a high index of suspicion.
