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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

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  1. Mohammad Ordikhani & Mohammad Saniee Abadeh & Christof Prugger & Razieh Hassannejad & Noushin Mohammadifard & Nizal Sarrafzadegan, 2022. "An evolutionary machine learning algorithm for cardiovascular disease risk prediction," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-16, July.
  2. Emily J MacKay & Michael D Stubna & Corey Chivers & Michael E Draugelis & William J Hanson & Nimesh D Desai & Peter W Groeneveld, 2021. "Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-14, June.
  3. Mirza Rizwan Sajid & Bader A. Almehmadi & Waqas Sami & Mansour K. Alzahrani & Noryanti Muhammad & Christophe Chesneau & Asif Hanif & Arshad Ali Khan & Ahmad Shahbaz, 2021. "Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
  4. N Salet & A Gökdemir & J Preijde & C H van Heck & F Eijkenaar, 2024. "Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care cardiovascular patients," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-17, July.
  5. Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.
  6. Gian Luca Di Tanna & Heidi Wirtz & Karen L Burrows & Gary Globe, 2020. "Evaluating risk prediction models for adults with heart failure: A systematic literature review," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-23, January.
  7. Mahade Hasan & Farhana Yasmin & Md Mehedi Hassan & Xue Yu & Soniya Yeasmin & Herat Joshi & Sheikh Mohammed Shariful Islam, 2025. "Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-34, January.
  8. Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju & Penmetsa Naveena Devi, 2024. "AI-Assisted Medical Imaging and Heart Disease Diagnosis: A Deep Learning Approach for Automated Analysis and Enhanced Prediction Using Ensemble Classifiers," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 210-229.
  9. Rafał Niemiec & Irmina Morawska & Maria Stec & Wiktoria Kuczmik & Andrzej S. Swinarew & Arkadiusz Stanula & Katarzyna Mizia-Stec, 2022. "ARNI in HFrEF—One-Centre Experience in the Era before the 2021 ESC HF Recommendations," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
  10. Roshan Karri & Yi-Ping Phoebe Chen & Aidan J C Burrell & Jahan C Penny-Dimri & Tessa Broadley & Tony Trapani & Adam M Deane & Andrew A Udy & Mark P Plummer & for the SPRINT-SARI Australia Investigator, 2022. "Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-15, October.
  11. Dohyun Kim & Sungmin You & Soonwon So & Jongshill Lee & Sunhyun Yook & Dong Pyo Jang & In Young Kim & Eunkyoung Park & Kyeongwon Cho & Won Chul Cha & Dong Wook Shin & Baek Hwan Cho & Hoon-Ki Park, 2018. "A data-driven artificial intelligence model for remote triage in the prehospital environment," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
  12. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
  13. Syed Waseem Abbas Sherazi & Jang-Whan Bae & Jong Yun Lee, 2021. "A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary ," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
  14. Francesco Cappelli & Gianfranco Castronuovo & Salvatore Grimaldi & Vito Telesca, 2024. "Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases," IJERPH, MDPI, vol. 21(7), pages 1-21, July.
  15. Eunji Koh & Younghoon Kim, 2022. "Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features," IJERPH, MDPI, vol. 19(22), pages 1-16, November.
  16. Ying Wang & Zhicheng Du & Wayne R. Lawrence & Yun Huang & Yu Deng & Yuantao Hao, 2019. "Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population," IJERPH, MDPI, vol. 16(23), pages 1-13, December.
  17. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.
  18. Pablo Gonzalez Ginestet & Ales Kotalik & David M. Vock & Julian Wolfson & Erin E. Gabriel, 2021. "Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 51-65, January.
  19. Salvatore Tedesco & Martina Andrulli & Markus Åkerlund Larsson & Daniel Kelly & Antti Alamäki & Suzanne Timmons & John Barton & Joan Condell & Brendan O’Flynn & Anna Nordström, 2021. "Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults," IJERPH, MDPI, vol. 18(23), pages 1-18, December.
  20. Ajay Dev & Sanjay Kumar Malik, 2021. "Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data: ABC-DNN for Prediction of Stroke," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(5), pages 67-83, September.
  21. Stephen F Weng & Luis Vaz & Nadeem Qureshi & Joe Kai, 2019. "Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
  22. Woo Suk Hong & Adrian Daniel Haimovich & R Andrew Taylor, 2018. "Predicting hospital admission at emergency department triage using machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
  23. Hoa Thi Nguyen & Claudia M. Denkinger & Stephan Brenner & Lisa Koeppel & Lucia Brugnara & Robin Burk & Michael Knop & Till Bärnighausen & Andreas Deckert & Manuela De Allegri, 2023. "Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(9), pages 1545-1559, December.
  24. Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
  25. Adrian Richter & Julia Truthmann & Jean-François Chenot & Carsten Oliver Schmidt, 2021. "Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
  26. José G Fuentes Cabrera & Hugo A Pérez Vicente & Sebastián Maldonado & Jonás Velasco, 2023. "Combination of unsupervised discretization methods for credit risk," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-18, November.
  27. Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
  28. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
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