Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pone.0232969
Download full text from publisher
References listed on IDEAS
- Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
- Laura Acion & Diana Kelmansky & Mark van der Laan & Ethan Sahker & DeShauna Jones & Stephan Arndt, 2017. "Use of a machine learning framework to predict substance use disorder treatment success," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
- Catherine Lee & Sebastien Haneuse & Hai-Lin Wang & Sherri Rose & Stephen R Spellman & Michael Verneris & Katharine C Hsu & Katharina Fleischhauer & Stephanie J Lee & Reza Abdi, 2018. "Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- Ali Unlu & Abdulhamit Subasi, 2025. "Substance use prediction using artificial intelligence techniques," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-40, February.
- 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.
- 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.
- Yuki Goshima & Shinji Nakaoka & Kazuteru Ohashi & Hisashi Sakamaki & Kazuko Shibuya & Akira Shibuya, 2020. "A mathematical model for dynamics of soluble form of DNAM-1 as a biomarker for graft-versus-host disease," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Sheelu Sagar & Rohit Rastogi & Vikas Garg & Ishwar V. Basavaraddi, 2022. "Impact of Meditation on Quality of Life of Employees," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-16, January.
- 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.
- Franck Jaotombo & Vanessa Pauly & Guillaume Fond & Veronica Orleans & Pascal Auquier & Badih Ghattas & Laurent Boyer, 2023. "Machine-learning prediction for hospital length of stay using a French medico-administrative database," Post-Print hal-04325691, HAL.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0232969. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.