AIMC Topic: Male

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Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea.

Journal of preventive medicine and public health = Yebang Uihakhoe chi
OBJECTIVES: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone.

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

European journal of clinical nutrition
BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which pat...

Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

Radiological physics and technology
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...

Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.

JACC. Heart failure
BACKGROUND: The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF).

Inter- and intra-rater reliability of cognitive assessment conducted by assistive robot for older adults living in the community: a preliminary study.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
BACKGROUND: The purpose of this study was to reveal inter- and intra-rater reliability of the detailed evaluation of cognitive function by assistive robot for older adults.

Machine learning-based prediction of sarcopenia in community-dwelling middle-aged and older adults: findings from the CHARLS.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
BACKGROUND: Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user-friendly ...

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.

Computers in biology and medicine
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory...

A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Epilepsia
OBJECTIVE: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective ...

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology.

Ecotoxicology and environmental safety
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine l...

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.

Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop...