AIMC Topic: Social Determinants of Health

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Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.

JMIR research protocols
BACKGROUND: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.

Social determinants of health and disparities in spine surgery: a 10-year analysis of 8,565 cases using ensemble machine learning and multilayer perceptron.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: The influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming ...

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.

Journal of medical systems
Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This sy...

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal of substance use and addiction treatment
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...

Charting a Path to the Quintuple Aim: Harnessing AI to Address Social Determinants of Health.

International journal of environmental research and public health
The Quintuple Aim seeks to improve healthcare by addressing social determinants of health (SDOHs), which are responsible for 70-80% of medical outcomes. SDOH-related concerns have traditionally been addressed through referrals to social workers and c...

Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing.

Journal of racial and ethnic health disparities
PURPOSE: This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processin...

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

Journal of biomedical informatics
OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different dise...

Machine Learning Approach to Study Social Determinants of Chronic Illness in India: A Comparative Analysis.

Indian journal of public health
BACKGROUND: Several studies on noncommunicable diseases (NCDs) have been carried out worldwide, the basis of most of which is the identification of risk factors-modifiable (or behavioral) and metabolic. Majority of the NCDs are due to sociodemographi...

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

Angiology
We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from...

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.

Journal of biomedical informatics
OBJECTIVE: To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning.