AIMC Topic: Patient Compliance

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Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models.

Journal of the American College of Radiology : JACR
OBJECTIVE: To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of ma...

Mammography Compliance for Arizona and New Mexico Hispanic and American Indian Women 2016-2018.

International journal of environmental research and public health
Hispanic and American Indian (AI) women experience lower breast cancer incidence than non-Hispanic White (NHW) women, but later-stage diagnoses and lower survival rates, suggesting issues with screening and healthcare access. Between 1999-2015, NHW b...

Development of a national deep learning-based auto-segmentation model for the heart on clinical delineations from the DBCG RT nation cohort.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: This study aimed at investigating the feasibility of developing a deep learning-based auto-segmentation model for the heart trained on clinical delineations.

Ethical Considerations of Using ChatGPT in Health Care.

Journal of medical Internet research
ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal...

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms.

International journal of environmental research and public health
In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predi...

Improved patient satisfaction and diagnostic accuracy in skin diseases with a Visual Clinical Decision Support System-A feasibility study with general practitioners.

PloS one
Patient satisfaction is an important indicator of health care quality, and it remains an important goal for optimal treatment outcomes to reduce the level of misdiagnoses and inappropriate or absent therapeutic actions. Digital support tools for diff...

Applying machine learning to predict future adherence to physical activity programs.

BMC medical informatics and decision making
BACKGROUND: Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured...

Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications.

Yearbook of medical informatics
OBJECTIVES: To summarise the state of the art during the year 2018 in consumer health informatics and education, with a special emphasis on the special topic of the International Medical Informatics Association (IMIA) Yearbook for 2019: "Artificial i...

Artificial Intelligence for Clinical Trial Design.

Trends in pharmacological sciences
Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical developm...

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Journal of clinical monitoring and computing
Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting ado...