AIMC Topic: Humans

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Game Design, Effectiveness, and Implementation of Serious Games Promoting Aspects of Mental Health Literacy Among Children and Adolescents: Systematic Review.

JMIR mental health
BACKGROUND: The effects of traditional health-promoting and preventive interventions in mental health and mental health literacy are often attenuated by low adherence and user engagement. Gamified approaches such as serious games (SGs) may be useful ...

Systematic Identification of Caregivers of Patients Living With Dementia in the Electronic Health Record: Known Contacts and Natural Language Processing Cohort Study.

Journal of medical Internet research
BACKGROUND: Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhancing care coordination, and advancing research and population health efforts in caregiving. Despite EH...

Enhancing Cardiopulmonary Resuscitation Quality Using a Smartwatch: Neural Network Approach for Algorithm Development and Validation.

JMIR mHealth and uHealth
BACKGROUND: Sudden cardiac arrest is a major cause of mortality, necessitating immediate and high-quality cardiopulmonary resuscitation (CPR) for improved survival rates. High-quality CPR is defined by chest compressions at a rate of 100-120 per minu...

The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Over the past few years, radiomics for the detection of intrahepatic cholangiocarcinoma (ICC) has been extensively studied. However, systematic evidence is lacking in the use of radiomics in this domain, which hinders its further developm...

Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).

International ophthalmology
PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.

Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.

Mikrochimica acta
Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, including culture-dependent methods, nucleic acid amplifications (including quantitative PCR), DNA microar...

Referral patterns, influencing factors, and satisfaction related to referrals of patients with rheumatic diseases to other healthcare professionals: an online survey of rheumatologists.

Rheumatology international
Managing rheumatic diseases requires teamwork, but referral patterns and challenges remain poorly understood. This study explored rheumatologists' perspectives on referral patterns in the Gulf countries. We conducted a web-based, 21-question cross-se...

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence.

JCO precision oncology
PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with rising incidence and mortality. Despite optimal treatment, 15%-20% of all patients will recur. To better select patients for adjuvant therapy, it is impo...

Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review and meta-analysis.

Frontiers in endocrinology
INTRODUCTION: In recent years, artificial intelligence (AI) tools have become widely studied for thyroid ultrasonography (USG) classification. The real-world applicability of these developed tools as pre-operative diagnostic aids is limited due to mo...

TCN-QV: an attention-based deep learning method for long sequence time-series forecasting of gold prices.

PloS one
Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely ...