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Patient Compliance

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Is Treatment Readiness Associated With Substance Use Treatment Engagement? An Exploratory Study.

Journal of drug education
With nearly 8.2% of Americans experiencing substance use disorders (SUDs), a need exists for effective SUD treatment and for strategies to assist treatment participants to complete treatment programs (Chandler, Fletcher, & Volkow, 2009). The purpose ...

Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.

Computer methods and programs in biomedicine
BACKGROUND: In the time since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in the formulation of an effective and efficient strategy to improve the participation rate has been growing. The aim ...

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Journal of diabetes science and technology
BACKGROUND: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of resea...

Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch.

Physiological measurement
OBJECTIVE: Participation in a physical therapy program is considered one of the greatest predictors of successful conservative management of common shoulder disorders. However, adherence to these protocols is often poor and typically worse for unsupe...

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...

Comparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapy.

BMC medical informatics and decision making
BACKGROUND: Patients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences f...

Using Machine Learning for Personalized Patient Adherence Level Determination.

Studies in health technology and informatics
The paper deals with using a machine-learning algorithm for patient adherence level determination. For this purpose, we developed a neural network using the Python language, Keras library, and PyCharm platform. We analyzed different medical data coll...

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...

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...