Moonstone: a novel natural language processing system for inferring social risk from clinical narratives

• Published in: Journal of biomedical semantics Vol. 10; no. 1; pp. 6 - 10
• Main Authors: Conway, Mike, Keyhani, Salomeh, Christensen, Lee, South, Brett R., Vali, Marzieh, Walter, Louise C., Mowery, Danielle L., Abdelrahman, Samir, Chapman, Wendy W.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.04.2019; BioMed Central; BMC
• Subjects: Alcohol; Algorithms; Artificial intelligence; Automation; Electronic health records; Electronic medical records; Electronic records; Emergency medical care; Emergency medical services; Health; Health aspects; Health care; Hospital admission and discharge; Hospitals; Housing; Humans; Identification; Information processing; Language; Life expectancy; Life span; Medical records; Narratives; Natural Language Processing; Nursing; Primary care; Primary care nursing; Quality of life; Risk analysis; Risk Factors; Semantics; Social determinants of health; Social Environment; Social interactions; Social support; Software; Text analysis; Social determinants of health; Natural language processing; Software
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-019-0198-0

Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information - in this case, housing situation, living alone, and social support - from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation. An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (housing situation, living alone, and social support). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (homeless/marginally housed) to 0.98 (lives at home/not homeless), accuracy scores ranging from 0.63 (lives in facility) to 0.95 (lives alone), and sensitivity (i.e. recall) scores ranging from 0.75 (lives in facility) to 0.97 (lives alone). The Moonstone system is - to the best of our knowledge - the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (lives in facility) to excellent (lives alone) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.