Classification of patients with chronic disease by activation level using machine learning methods
Published 2023 View Full Article
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Title
Classification of patients with chronic disease by activation level using machine learning methods
Authors
Keywords
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Journal
Health Care Management Science
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-10-12
DOI
10.1007/s10729-023-09653-4
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- (2020) Levi N. Bonnell et al. Journal of the American Board of Family Medicine
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- (2019) Mo Zhou et al. BMC Medical Informatics and Decision Making
- Factors associated with patient activation in a Turkish population with diabetes and/or hypertension
- (2019) S Sakarya et al. EUROPEAN JOURNAL OF PUBLIC HEALTH
- What are the effective elements in patient-centered and multimorbidity care? A scoping review
- (2018) Marie-Eve Poitras et al. BMC HEALTH SERVICES RESEARCH
- A support vector machine-based ensemble algorithm for breast cancer diagnosis
- (2018) Haifeng Wang et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
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- (2018) John Wallert et al. JOURNAL OF MEDICAL INTERNET RESEARCH
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- (2018) Sarah Chew et al. PATIENT EDUCATION AND COUNSELING
- Patient Activation Changes as a Potential Signal for Changes in Health Care Costs: Cohort Study of US High-Cost Patients
- (2018) Ann Lindsay et al. JOURNAL OF GENERAL INTERNAL MEDICINE
- Determinants of patient activation in a community sample of breast and prostate cancer survivors
- (2017) Denalee O'Malley et al. PSYCHO-ONCOLOGY
- Health Literacy, Diabetes Prevention, and Self-Management
- (2017) Joanne Protheroe et al. Journal of Diabetes Research
- What's in a name? A call to reframe non-communicable diseases
- (2017) Luke N Allen et al. Lancet Global Health
- Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques
- (2017) Evanthia E. Tripoliti et al. Computational and Structural Biotechnology Journal
- Adding A Measure Of Patient Self-Management Capability To Risk Assessment Can Improve Prediction Of High Costs
- (2016) Judith H. Hibbard et al. HEALTH AFFAIRS
- The Patient Engagement Imperative
- (2016) Alan R. Weil HEALTH AFFAIRS
- Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
- (2016) Ziad Obermeyer et al. NEW ENGLAND JOURNAL OF MEDICINE
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- (2015) Hayley McBain et al. BMC HEALTH SERVICES RESEARCH
- When Patient Activation Levels Change, Health Outcomes And Costs Change, Too
- (2015) Jessica Greene et al. HEALTH AFFAIRS
- Health policy analysis for prevention and control of cardiovascular diseases and diabetes mellitus in Turkey
- (2014) Bulent Kilic et al. International Journal of Public Health
- Patient engagement as a risk factor in personalized health care: a systematic review of the literature on chronic disease
- (2014) Leigh Simmons et al. Genome Medicine
- The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ)
- (2013) Richard H Osborne et al. BMC PUBLIC HEALTH
- What The Evidence Shows About Patient Activation: Better Health Outcomes And Care Experiences; Fewer Data On Costs
- (2013) Judith H. Hibbard et al. HEALTH AFFAIRS
- Patients With Lower Activation Associated With Higher Costs; Delivery Systems Should Know Their Patients’ ‘Scores’
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- Rx For The ‘Blockbuster Drug’ Of Patient Engagement
- (2013) Susan Dentzer HEALTH AFFAIRS
- Patient And Family Engagement: A Framework For Understanding The Elements And Developing Interventions And Policies
- (2013) Kristin L. Carman et al. HEALTH AFFAIRS
- Effect of Patient Activation on Self-Management in Patients With Heart Failure
- (2013) Martha J. Shively et al. Journal of Cardiovascular Nursing
- Skill Set or Mind Set? Associations between Health Literacy, Patient Activation and Health
- (2013) Samuel G. Smith et al. PLoS One
- Patient empowerment: The need to consider it as a measurable patient-reported outcome for chronic conditions
- (2012) Marion McAllister et al. BMC HEALTH SERVICES RESEARCH
- Why Does Patient Activation Matter? An Examination of the Relationships Between Patient Activation and Health-Related Outcomes
- (2011) Jessica Greene et al. JOURNAL OF GENERAL INTERNAL MEDICINE
- Patient Activation in Primary Healthcare
- (2011) Sabrina T. Wong et al. MEDICAL CARE
- Incorporating domain knowledge into data mining classifiers: An application in indirect lending
- (2008) Atish P. Sinha et al. DECISION SUPPORT SYSTEMS
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