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For: Barrett M, Boyne J, Brandts J, Brunner-La Rocca HP, De Maesschalck L, De Wit K, Dixon L, Eurlings C, Fitzsimons D, Golubnitschaja O, Hageman A, Heemskerk F, Hintzen A, Helms TM, Hill L, Hoedemakers T, Marx N, McDonald K, Mertens M, Müller-Wieland D, Palant A, Piesk J, Pomazanskyi A, Ramaekers J, Ruff P, Schütt K, Shekhawat Y, Ski CF, Thompson DR, Tsirkin A, van der Mierden K, Watson C, Zippel-Schultz B. Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. EPMA J 2019;10:445-64. [PMID: 31832118 DOI: 10.1007/s13167-019-00188-9] [Cited by in Crossref: 28] [Cited by in F6Publishing: 21] [Article Influence: 9.3] [Reference Citation Analysis]
Number Citing Articles
1 Yin T, Zheng H, Ma T, Tian X, Xu J, Li Y, Lan L, Liu M, Sun R, Tang Y, Liang F, Zeng F. Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine. EPMA Journal. [DOI: 10.1007/s13167-022-00271-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
2 Mousa R, Hammad E. Cost-effectiveness of pharmacist-led care versus usual care in type 2 diabetic Jordanians: a Markov modeling of cardiovascular diseases prevention. Expert Rev Pharmacoecon Outcomes Res 2021;21:1069-79. [PMID: 33213221 DOI: 10.1080/14737167.2021.1838900] [Reference Citation Analysis]
3 Birkenbihl C, Emon MA, Vrooman H, Westwood S, Lovestone S, Hofmann-Apitius M, Fröhlich H; AddNeuroMed Consortium., Alzheimer’s Disease Neuroimaging Initiative. Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice. EPMA J 2020;11:367-76. [PMID: 32843907 DOI: 10.1007/s13167-020-00216-z] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
4 Kazemi Majd F, Gavgani VZ, Golmohammadi A, Jafari-Khounigh A. Effect of physician prescribed information on hospital readmission and death after discharge among patients with health failure: A randomized controlled trial. Health Informatics J 2021;27:1460458221996409. [PMID: 33657912 DOI: 10.1177/1460458221996409] [Reference Citation Analysis]
5 Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021;41:1123-33. [PMID: 34950987 DOI: 10.1007/s11596-021-2485-0] [Reference Citation Analysis]
6 Kinkorová J, Topolčan O. Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine. EPMA J 2020;11:333-41. [PMID: 32849924 DOI: 10.1007/s13167-020-00213-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
7 Helms TM, Bosch R, Hansen C, Willhöft C, Zippel-Schultz B, Karle C, Schwab JO. [Structural requirements and prerequisites for outpatient implantation of defibrillators, devices for cardiac resynchronization and event recorders]. Herzschrittmacherther Elektrophysiol 2021;32:227-35. [PMID: 33982176 DOI: 10.1007/s00399-021-00764-5] [Reference Citation Analysis]
8 Zhan X, Li J, Guo Y, Golubnitschaja O. Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine. EPMA J 2021;:1-27. [PMID: 34876936 DOI: 10.1007/s13167-021-00265-y] [Reference Citation Analysis]
9 Lee KW, Shin D. Relationships of Dietary Factors with Obesity, Hypertension, and Diabetes by Regional Type among Single-Person Households in Korea. Nutrients 2021;13:1218. [PMID: 33917110 DOI: 10.3390/nu13041218] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Zippel-Schultz B, Palant A, Eurlings C, F Ski C, Hill L, Thompson DR, Fitzsimons D, Dixon LJ, Brandts J, Schuett KA, de Maesschalck L, Barrett M, Furtado da Luz E, Hoedemakers T, Helms TM, Brunner-La Rocca HP; PASSION-HF consortium. Determinants of acceptance of patients with heart failure and their informal caregivers regarding an interactive decision-making system: a qualitative study. BMJ Open 2021;11:e046160. [PMID: 34135043 DOI: 10.1136/bmjopen-2020-046160] [Reference Citation Analysis]
11 Silva-Cardoso J, Juanatey JRG, Comin-Colet J, Sousa JM, Cavalheiro A, Moreira E. The Future of Telemedicine in the Management of Heart Failure Patients. Card Fail Rev 2021;7:e11. [PMID: 34136277 DOI: 10.15420/cfr.2020.32] [Reference Citation Analysis]
12 Koklesova L, Liskova A, Samec M, Zhai K, Al-Ishaq RK, Bugos O, Šudomová M, Biringer K, Pec M, Adamkov M, Hassan STS, Saso L, Giordano FA, Büsselberg D, Kubatka P, Golubnitschaja O. Protective Effects of Flavonoids Against Mitochondriopathies and Associated Pathologies: Focus on the Predictive Approach and Personalized Prevention. Int J Mol Sci 2021;22:8649. [PMID: 34445360 DOI: 10.3390/ijms22168649] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Johnson AE, Brewer LC, Echols MR, Mazimba S, Shah RU, Breathett K. Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure. Heart Failure Clinics 2022. [DOI: 10.1016/j.hfc.2021.11.001] [Reference Citation Analysis]
14 Ammar A, Bouaziz B, Trabelsi K, Glenn JM, Zmijewski P, Müller P, Chtourou H, Jmaiel M, Chamari K, Driss T, Hökelmann A. Applying digital technology to promote active and healthy confinement lifestyle during pandemics in the elderly. Biol Sport 2021;38:391-6. [PMID: 34475622 DOI: 10.5114/biolsport.2021.100149] [Cited by in Crossref: 14] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
15 Ski CF, Zippel-Schultz B, De Maesschalck L, Hoedemakers T, Schütt K, Thompson DR, Brunner La-Rocca HP. COVID-19 shapes the future for management of patients with chronic cardiac conditions. Digit Health 2021;7:2055207621991711. [PMID: 33623705 DOI: 10.1177/2055207621991711] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
16 Polyakova EA, Mikhaylov EN, Sonin DL, Cheburkin YV, Galagudza MM. Neurohumoral, cardiac and inflammatory markers in the evaluation of heart failure severity and progression. J Geriatr Cardiol 2021;18:47-66. [PMID: 33613659 DOI: 10.11909/j.issn.1671-5411.2021.01.007] [Reference Citation Analysis]
17 Ski CF, Thompson DR, Brunner-La Rocca HP. Putting AI at the centre of heart failure care. ESC Heart Fail 2020;7:3257-8. [PMID: 32558251 DOI: 10.1002/ehf2.12813] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
18 Tahri Sqalli M, Al-Thani D. On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review. Healthcare (Basel) 2020;8:E313. [PMID: 32883036 DOI: 10.3390/healthcare8030313] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
19 Laad M, Kotecha K, Patil K, Pise R. Cardiac Diagnosis with Machine Learning: A Paradigm Shift in Cardiac Care. Applied Artificial Intelligence. [DOI: 10.1080/08839514.2022.2031816] [Reference Citation Analysis]
20 Aggarwal N, Ahmed M, Basu S, Curtin JJ, Evans BJ, Matheny ME, Nundy S, Sendak MP, Shachar C, Shah RU, Thadaney-israni S. Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic. NAM Perspectives. [DOI: 10.31478/202011f] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
21 Hill L, Lambrinou E, Moser DK, Beattie JM. The COVID-19 pandemic: challenges in providing supportive care to those with cardiovascular disease in a time of plague. Curr Opin Support Palliat Care 2021;15:147-53. [PMID: 33843761 DOI: 10.1097/SPC.0000000000000552] [Reference Citation Analysis]
22 Moses JC, Adibi S, Angelova M, Islam SMS. Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review. ASI 2022;5:51. [DOI: 10.3390/asi5030051] [Reference Citation Analysis]
23 Alqarni NA, Alqahtani SS, Alhumaidi SA, Almutairi IM, Alshabanah M, Alrajhi D, Alsmadi MK, Almarashdeh I. Developing a Platform for Chronic Diseases Awareness. IJSRST. [DOI: 10.32628/ijsrst207160] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
24 Berei T, Forsyth P, Balakumaran K, Harshaw-Ellis K, Koshman S, Rasmusson K. Implementing Nonphysician Provider Guideline-Directed Medical Therapy Heart Failure Clinics: A Multi-National Imperative. J Card Fail 2021;27:896-906. [PMID: 34364666 DOI: 10.1016/j.cardfail.2021.06.001] [Reference Citation Analysis]
25 Shaffer A, Cogswell R, John R. Future developments in left ventricular assist device therapy. J Thorac Cardiovasc Surg 2021;162:605-11. [PMID: 33293063 DOI: 10.1016/j.jtcvs.2020.07.125] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
26 Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med 2021;135:104660. [PMID: 34346319 DOI: 10.1016/j.compbiomed.2021.104660] [Reference Citation Analysis]
27 Gingele AJ, Brandts L, Vossen K, Knackstedt C, Boyne J, Brunner-La Rocca HP. Prognostic value of signs and symptoms in heart failure patients using remote telemonitoring. J Telemed Telecare 2021;:1357633X211039404. [PMID: 34516318 DOI: 10.1177/1357633X211039404] [Reference Citation Analysis]
28 Haleem A, Javaid M, Singh RP, Suman R. Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. Sustainable Operations and Computers 2021;2:71-8. [DOI: 10.1016/j.susoc.2021.04.003] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
29 Sammani A, Baas AF, Asselbergs FW, Te Riele ASJM. Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics. J Clin Med 2021;10:921. [PMID: 33652931 DOI: 10.3390/jcm10050921] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Jasinska-piadlo A, Bond R, Biglarbeigi P, Brisk R, Campbell P, Mceneaneny D. What can machines learn about heart failure? A systematic literature review. Int J Data Sci Anal. [DOI: 10.1007/s41060-021-00300-1] [Reference Citation Analysis]
31 Golubnitschaja O, Liskova A, Koklesova L, Samec M, Biringer K, Büsselberg D, Podbielska H, Kunin AA, Evsevyeva ME, Shapira N, Paul F, Erb C, Dietrich DE, Felbel D, Karabatsiakis A, Bubnov R, Polivka J, Polivka J Jr, Birkenbihl C, Fröhlich H, Hofmann-Apitius M, Kubatka P. Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021. EPMA J 2021;:1-22. [PMID: 34422142 DOI: 10.1007/s13167-021-00251-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
32 Otte K, Ellermeyer T, Suzuki M, Röhling HM, Kuroiwa R, Cooper G, Mansow-Model S, Mori M, Zimmermann H, Brandt AU, Paul F, Hirano S, Kuwabara S, Schmitz-Hübsch T. Cultural bias in motor function patterns: Potential relevance for predictive, preventive, and personalized medicine. EPMA J 2021;12:91-101. [PMID: 33782636 DOI: 10.1007/s13167-021-00236-3] [Reference Citation Analysis]
33 Taloba AI, Abd El-aziz RM, Alshanbari HM, El-bagoury AH, Shankar K. Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning. Journal of Healthcare Engineering 2022;2022:1-10. [DOI: 10.1155/2022/7969220] [Reference Citation Analysis]
34 Amin H, Weerts J, Brunner-La Rocca HP, Knackstedt C, Sanders-van Wijk S. Future perspective of heart failure care: benefits and bottlenecks of artificial intelligence and eHealth. Future Cardiol 2021. [PMID: 33576271 DOI: 10.2217/fca-2021-0008] [Reference Citation Analysis]