AIMC Topic: Patient Acceptance of Health Care
Clear Filters Showing 61 to 70 of 88 articles
Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.
BMC medical informatics and decision making
May 26, 2025
INTRODUCTION: Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, particularly in low- and middle-income countries (LMICs). It is preventable through early screening, but i...
Automated and AI-Based Communication in the GP Practice as a Replacement for Personal Doctor-Patient Contact - A Patient Survey on Acceptance.
Studies in health technology and informatics
May 15, 2025
BACKGROUND/AIM: The study examines patient acceptance of automated and AI-based communication as a replacement for personal doctor-patient contact in general practice.
Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China.
BMC public health
May 14, 2025
BACKGROUND: With the progress of economic and social development, the demand for healthcare has increased, making the rational allocation of health resources and the efficient utilization of services crucial for public health. However, mismatches bet...
Predicting Prostate Cancer Diagnosis Using Machine Learning Analysis of Healthcare Utilization Patterns.
Studies in health technology and informatics
Apr 8, 2025
This study investigated healthcare utilization patterns prior to prostate cancer diagnoses, aiming to develop machine learning models for early prediction of cancer diagnosis. Data from the All of Us Research Program was used, focusing on adult patie...
Using Machine Learning to Predict Uptake to an Online Self-Guided Intervention for Stress During the COVID-19 Pandemic.
Stress and health : journal of the International Society for the Investigation of Stress
Apr 1, 2025
Online self-guided interventions appear efficacious for alleviating some mental health concerns. However, among persons who are offered online interventions, only a fraction access them (i.e., achieve uptake). Machine learning methods may be useful t...
Acceptance of Using Artificial Intelligence and Digital Technology for Mental Health Interventions: The Development and Initial Validation of the UTAUT-AI-DMHI.
Clinical psychology & psychotherapy
Jan 1, 2025
Digital health technologies are being increasingly integrated into mental healthcare. This means that patients have different treatment options, and clinicians need to consider different ways of supporting their patients too. The adoption of Digital ...
Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.
The American journal of sports medicine
Jul 1, 2024
BACKGROUND: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, me...
Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases.
Inflammatory bowel diseases
May 2, 2024
BACKGROUND: Inflammatory bowel disease (IBD) is associated with increased health care utilization. Forecasting of high resource utilizers could improve resource allocation. In this study, we aimed to develop machine learning models (1) to cluster pat...
Acceptance of Telepresence Robotics, Telecare and Teletherapy Among Stroke Patients, Relatives and Therapy Staff.
Studies in health technology and informatics
Apr 26, 2024
BACKGROUND: Stroke as a cause of disability in adulthood causes an increasing demand for therapy and care services, including telecare and teletherapy.
Predicting Avoidable Health Care Utilization: Practical Considerations for Artificial Intelligence/Machine Learning Models in Population Health.
Mayo Clinic proceedings
Apr 1, 2022
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