Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.

Background: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Objective: This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health.

Methods: The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete.

Intraoperative Type B Aortic Dissection With Malperfusion in a Patient With Marfan Syndrome.

We present the case of a 72-year-old man diagnosed with an aortic root aneurysm who was then diagnosed with Marfan syndrome. The patient suffered an intraoperative type B dissection with lower extremity malperfusion managed with an axillary-bifemoral extra-anatomic bypass.

Using large language models for extracting stressful life events to assess their impact on preventive colon cancer screening adherence.

Background: Increase in early onset colorectal cancer makes adherence to screening a significant public health concern, with various social determinants playing a crucial role in its incidence, diagnosis, treatment, and outcomes. Stressful life events, such as divorce, marriage, or sudden loss of job, have a unique position among the social determinants of health.

Methods: We applied a large language model (LLM) to social history sections of clinical notes in the health records database of the Medical University of South Carolina to extract recent stressful life events and assess their impact on colorectal cancer screening adherence.

Machine learning in healthcare citizen science: A scoping review.

Objectives: This scoping review aims to clarify the definition and trajectory of citizen-led scientific research (so-called citizen science) within the healthcare domain, examine the degree of integration of machine learning (ML) and the participation levels of citizen scientists in health-related projects.

Materials And Methods: In January and September 2024 we conducted a comprehensive search in PubMed, Scopus, Web of Science, and EBSCOhost platform for peer-reviewed publications that combine citizen science and machine learning (ML) in healthcare. Articles were excluded if citizens were merely passive data providers or if only professional scientists were involved.

Machine learning derived physical activity in preschool children with developmental coordination disorder.

Aim: To compare the device-measured physical activity behaviours of preschool children with typical motor development to those with probable developmental coordination disorder (pDCD) and at risk for developmental coordination disorder (DCDr).

Method: A total of 497 preschool children (4-5 years) in the Coordination and Activity Tracking in CHildren (CATCH) study completed repeated motor assessments and wore an ActiGraph GT3X on the right hip at baseline for 1 week. We calculated physical activity metrics from raw accelerometer data using a validated random forest classification machine learning model for preschool-age children.