AIMC Topic: Middle Aged

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Improving the mapping of leisure-time physical activity inequities: the use of artificial intelligence to advance estimates of small-areas in Brazil.

Public health
OBJECTIVE: We estimated the prevalence of leisure-time physical activity (LTPA) in small areas of the city of Belo Horizonte and analyzed inequities across areas and between two time periods, 2009-2013 and 2014-2018.

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Psychiatry research
Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). The objective of this study is to develop artificial neural network (ANN) models to predict the indi...

Assessment of ChatGPT's adherence to EULAR diagnostic criteria and therapeutic protocols for rheumatoid arthritis at two distinct time points, 14 days apart, utilizing binary and multiple-choice inquiries.

Clinical rheumatology
OBJECTIVES: Artificial intelligence (AI) possesses considerable promise in healthcare to offer decision help in particular domains, including rheumatoid arthritis (RA). This study assesses the adherence of the advanced AI model ChatGPT-v4 to the Euro...

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individ...

Empowering individuals to adopt artificial intelligence for health information seeking: A latent profile analysis among users in Hong Kong.

Social science & medicine (1982)
RATIONALES: Using AI for health information seeking is a novel behavior, and as such, developing effective communication strategies to optimize AI adoption in this area presents challenges. To lay the groundwork, research is needed to map out users' ...

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

Artificial intelligence in medicine
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...

Development of a patient reported outcomes based machine learning model to predict recurrences in head and neck cancer.

Oral oncology
INTRODUCTION: Recurrence rates among Head and Neck Cancer (HNC) patients are high, with earlier detection associated with improved survival. Patient-reported outcomes (PROs) have increasingly been found to predict patient care needs. Here, we examine...

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

Clinical neurology and neurosurgery
OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for ...

Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The objective of this study was to develop an automated, accurate method of assessing coronary artery disease (CAD), including its severity and location, using deep-mining-based magnetocardiography (MCG) pattern features.