BACKGROUND: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting...
BACKGROUND: In recent years, the intersection of natural language processing (NLP) and public health has opened innovative pathways for investigating social determinants of health (SDOH) in textual datasets. Despite the promise of NLP in the SDOH dom...
BACKGROUND: Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates.
BACKGROUND: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.
International journal of environmental research and public health
40003519
In recent decades, technological shifts within the healthcare sector have significantly transformed healthcare management and utilization, introducing unprecedented possibilities that elevate quality of life. Organizational factors are recognized as ...
BACKGROUND: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that use real-world data (RWD) to enhance earl...
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, researchers have identified that social determinants of health (SDOH) (employment status, family inc...
BACKGROUND: Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), spec...
Journal of the American Medical Directors Association
40023505
OBJECTIVE: To identify self-reported social determinants of health (SDOH) among hospitalized patients that predict discharge to a skilled nursing facility (SNF).
Journal of the American Medical Informatics Association : JAMIA
40085013
OBJECTIVE: Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of th...