Check health professions papers: citations, claims and retractions

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

Ambale-Venkatesh, B., Yang, X., Wu, C. O., Liu, K., Hundley, W. G., McClelland, R., Gomes, A. S., Folsom, A. R., Shea, S., Guallar, E., et al. (2017) · Circulation Research

Artificial Intelligence in HealthcareCardiac Imaging and Diagnostics

Cited by 639

Uniqueness of medical data mining

Cios, K. J., & William Moore, G. (2002) · Artificial Intelligence in Medicine

Artificial Intelligence in HealthcarePrivacy-Preserving Technologies in Data

Cited by 639

Low Health Literacy and Evaluation of Online Health Information: A Systematic Review of the Literature

Diviani, N., van den Putte, B., Giani, S., & van Weert, J. C. (2015) · Journal of Medical Internet Research

Health Literacy and Information AccessibilityInformation Retrieval and Search Behavior

Cited by 638

Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals

Gagnon, M.-P., Desmartis, M., Labrecque, M., Car, J., Pagliari, C., Pluye, P., Frémont, P., Gagnon, J., Tremblay, N., & Légaré, F. (2012) · Journal of Medical Systems

Electronic Health Records SystemsTechnology Adoption and User Behaviour

Cited by 637

Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies

O’Connor, S., Hanlon, P., O’Donnell, C. A., Garcia, S., Glanville, J., & Mair, F. S. (2016) · BMC Medical Informatics and Decision Making

Mobile Health and mHealth ApplicationsHealth, Technology, Consumer Behavior

Cited by 636

The future of health behavior change research: What is needed to improve translation of research into health promotion practice?

Glasgow, R. E., Klesges, L. M., Dzewaltowski, D. A., Bull, S. S., & Estabrooks, P. (2004) · Annals of Behavioral Medicine

Health Policy Implementation ScienceBehavioral Health and Interventions

Cited by 635

Chronic pain epidemiology and its clinical relevance

van Hecke, O., Torrance, N., & Smith, B. H. (2013) · British Journal of Anaesthesia

Musculoskeletal pain and rehabilitationPain Mechanisms and Treatments

Cited by 632

What to Tell Cancer Patients: A Study of Medical Attitudes

Oken, D. (1961) · JAMA

Patient-Provider Communication in HealthcarePalliative Care and End-of-Life Issues

Cited by 632

Embodied health movements: new approaches to social movements in health

Brown, P., Zavestoski, S., McCormick, S., Mayer, B., Morello‐Frosch, R., & Gasior Altman, R. (2004) · Sociology of Health & Illness

Health, Medicine and SocietySocial Movements and Cultural Identity

Cited by 631

Acoustic correlates of breathy vocal quality: dysphonic voices and continuous speech.

Hillenbrand, J., & Houde, R. A. (1996) · Journal of Speech, Language, and Hearing Research

Voice and Speech DisordersPhonetics and Phonology Research

Cited by 631

Digital Health Equity and COVID-19: The Innovation Curve Cannot Reinforce the Social Gradient of Health

Crawford, A., & Serhal, E. (2020) · Journal of Medical Internet Research

Health Literacy and Information AccessibilityTelemedicine and Telehealth Implementation

Cited by 630

The mPower study, Parkinson disease mobile data collected using ResearchKit

Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., et al. (2016) · Scientific Data

Mobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Cited by 630

Developing a comprehensive definition of sustainability

Moore, J. E., Mascarenhas, A., Bain, J., & Straus, S. E. (2017) · Implementation Science

Health Policy Implementation ScienceHealth Sciences Research and Education

Cited by 630

Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates

Blak, B., Thompson, M., Dattani, H., & Bourke, A. (2011) · Journal of Innovation in Health Informatics

Medical Coding and Health InformationElectronic Health Records Systems

Cited by 630

Multidisciplinary biopsychosocial rehabilitation for chronic low back pain

Kamper, S. J., Apeldoorn, A. T., Chiarotto, A., Smeets, R. J. E. M., Ostelo, R. W., Guzman, J., & van Tulder, M. W. (2014) · Cochrane Database of Systematic Reviews

Musculoskeletal pain and rehabilitationMedical Practices and Rehabilitation

Cited by 629

Reduction of Pain Catastrophizing Mediates the Outcome of Both Physical and Cognitive-Behavioral Treatment in Chronic Low Back Pain

Smeets, R. J. E. M., Vlaeyen, J. W. S., Kester, A. D. M., & Knottnerus, J. A. (2006) · The Journal of Pain

Musculoskeletal pain and rehabilitationPain Management and Treatment

Cited by 629

Literacy and misunderstanding prescription drug labels

Davis, T. C., Wolf, M. S., Bass, P. F., Thompson, J. A., Tilson, H. H., Neuberger, M., & Parker, R. M. (2006) · Annals of Internal Medicine

Health Literacy and Information AccessibilityPharmacy and Medical Practices

Cited by 628

Standards of Evidence for Efficacy, Effectiveness, and Scale-up Research in Prevention Science: Next Generation

Gottfredson, D. C., Cook, T. D., Gardner, F. E. M., Gorman-Smith, D., Howe, G. W., Sandler, I. N., & Zafft, K. M. (2015) · Prevention Science

Health Policy Implementation ScienceEvaluation and Performance Assessment

Cited by 627

The Importance of Trunk Muscle Strength for Balance, Functional Performance, and Fall Prevention in Seniors: A Systematic Review

Granacher, U., Gollhofer, A., Hortobágyi, T., Kressig, R. W., & Muehlbauer, T. (2013) · Sports Medicine

Balance, Gait, and Falls PreventionNutrition and Health in Aging

Cited by 625

An exploration of the factors that influence the implementation of evidence into practice

Rycroft‐Malone, J., Harvey, G., Seers, K., Kitson, A., McCormack, B., & Titchen, A. (2004) · Journal of Clinical Nursing

Health Sciences Research and EducationHealth Policy Implementation Science

Cited by 623

Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function

Seminowicz, D. A., Wideman, T. H., Naso, L., Hatami-Khoroushahi, Z., Fallatah, S., Ware, M. A., Jarzem, P., Bushnell, M. C., Shir, Y., Ouellet, J. A., et al. (2011) · The Journal of Neuroscience

Musculoskeletal pain and rehabilitationPain Mechanisms and Treatments

Cited by 622

Pain catastrophizing predicts pain intensity, disability, and psychological distress independent of the level of physical impairment

Severeijns, R., Vlaeyen, J. W. S., van den Hout, M. A., & Weber, W. E. J. (2001) · The Clinical Journal of Pain

Musculoskeletal pain and rehabilitationPediatric Pain Management Techniques

Cited by 622

m-Health adoption by healthcare professionals: a systematic review

Gagnon, M.-P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016) · Journal of the American Medical Informatics Association

Mobile Health and mHealth ApplicationsHealth, Technology, Consumer Behavior

Cited by 622

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018) · Mobile Information Systems

Artificial Intelligence in HealthcareData Mining and Machine Learning Applications

Cited by 621