Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models
Recurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10969766/ |
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| author | Mohammad Alshraideh Arafat Al-Dhaqm Ahmad Alshammari Abedalrahman Alshraideh Bahaaldeen Alshraideh Bayan Mohamed Al-Fayoumi Maged Nasser |
| author_facet | Mohammad Alshraideh Arafat Al-Dhaqm Ahmad Alshammari Abedalrahman Alshraideh Bahaaldeen Alshraideh Bayan Mohamed Al-Fayoumi Maged Nasser |
| author_sort | Mohammad Alshraideh |
| collection | DOAJ |
| description | Recurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The goal of this analysis is to carry out advanced predictive analytics related to the problem of recurrent fragility fractures concerning several key features about each patient: age, sex, body mass index, physical activities, smoking status, and several others: T-score, along with biomarkers such as Vitamin D3, calcium levels, and the rest. Indeed, the results show that the features most indicative of recurrent fractures include age, T-score, physical activity, and glucocorticoid usage; greater emphasis has been laid on bone mineral density and lifestyle factors. The detailed feature importance analysis showed that age and T-score have the highest important values for fracture recurrence prediction, followed by physical activity and glucocorticoid treatment. The study finds that the proposed models achieved an accuracy of 91.3% (LLaMA 7GB) and 90.2% (Gamma 7GB), with ROC AUC scores of 93% and 92%, respectively. The study highlights that age, T-score, and glucocorticoid usage were the most significant predictive factors. These findings enable improved clinical decision-making for fracture prevention. This model can facilitate the identification of high-risk patients with recurrent fragility fractures by clinicians, thus allowing for targeted intervention and personalized treatment to reduce the risk of future fractures. The findings contribute to the development of fracture prediction, incorporating a wide array of clinical data that, in turn, will improve patient outcomes and their quality of life. |
| format | Article |
| id | doaj-art-8839b7bb2d6f4ad6a7ecfc4f1a3b2894 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8839b7bb2d6f4ad6a7ecfc4f1a3b28942025-08-20T02:29:31ZengIEEEIEEE Access2169-35362025-01-0113715037152010.1109/ACCESS.2025.356225910969766Advanced Prediction of Recurrent Fragility Fractures Using Large Language ModelsMohammad Alshraideh0https://orcid.org/0000-0002-2724-9290Arafat Al-Dhaqm1https://orcid.org/0000-0002-0729-2654Ahmad Alshammari2https://orcid.org/0009-0000-2051-2757Abedalrahman Alshraideh3https://orcid.org/0009-0008-8907-8010Bahaaldeen Alshraideh4https://orcid.org/0009-0008-9293-6426Bayan Mohamed Al-Fayoumi5Maged Nasser6https://orcid.org/0000-0003-3788-5722Artificial Intelligence Department, The University of Jordan, Amman, JordanSchool of Computer Science (SCS), Taylor’s University, Subang Jaya, MalaysiaDepartment of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi ArabiaEast Midlands Deanery, NHS, Nottingham, England, U.K.Department of Special Surgery, Division of Urology, The University of Jordan, Amman, JordanInformation Technology College, Lusail University, Lusail, QatarComputer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaRecurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The goal of this analysis is to carry out advanced predictive analytics related to the problem of recurrent fragility fractures concerning several key features about each patient: age, sex, body mass index, physical activities, smoking status, and several others: T-score, along with biomarkers such as Vitamin D3, calcium levels, and the rest. Indeed, the results show that the features most indicative of recurrent fractures include age, T-score, physical activity, and glucocorticoid usage; greater emphasis has been laid on bone mineral density and lifestyle factors. The detailed feature importance analysis showed that age and T-score have the highest important values for fracture recurrence prediction, followed by physical activity and glucocorticoid treatment. The study finds that the proposed models achieved an accuracy of 91.3% (LLaMA 7GB) and 90.2% (Gamma 7GB), with ROC AUC scores of 93% and 92%, respectively. The study highlights that age, T-score, and glucocorticoid usage were the most significant predictive factors. These findings enable improved clinical decision-making for fracture prevention. This model can facilitate the identification of high-risk patients with recurrent fragility fractures by clinicians, thus allowing for targeted intervention and personalized treatment to reduce the risk of future fractures. The findings contribute to the development of fracture prediction, incorporating a wide array of clinical data that, in turn, will improve patient outcomes and their quality of life.https://ieeexplore.ieee.org/document/10969766/Osteoporosisfractureslarge language modelsmachine learningrecurrent fragilitygamma model |
| spellingShingle | Mohammad Alshraideh Arafat Al-Dhaqm Ahmad Alshammari Abedalrahman Alshraideh Bahaaldeen Alshraideh Bayan Mohamed Al-Fayoumi Maged Nasser Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models IEEE Access Osteoporosis fractures large language models machine learning recurrent fragility gamma model |
| title | Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models |
| title_full | Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models |
| title_fullStr | Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models |
| title_full_unstemmed | Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models |
| title_short | Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models |
| title_sort | advanced prediction of recurrent fragility fractures using large language models |
| topic | Osteoporosis fractures large language models machine learning recurrent fragility gamma model |
| url | https://ieeexplore.ieee.org/document/10969766/ |
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