Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2025
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| author | Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko |
| author_facet | Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko |
| collection | Wiley Open Access |
| contents | Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko Analytical Science Advances ABSTRACTThe proportion of older people in the world's total population is expected to increase. Bone diseases are more prevalent in older people; therefore, the number of patients with such diseases is expected to increase worldwide. Artificial bone is a biomaterial used in the treatment of bone diseases. Artificial bones with high bone formation rates are desired; however, the results of artificial bone implantation vary. There are also ethical issues associated with animal experiments. Our purpose in this study is to predict the variation in bone formation rates. We created multiple sub‐datasets and constructed a machine learning model to predict the variation in bone formation rates by considering the results of multiple measurements. We also propose a metric, Jensen–Shannon (JS) divergence, to evaluate the accuracy of the model for predicting variation. We tested the validity of JS divergence by comparing combinations of explanatory variables. Additionally, we found an optimal combination of explanatory variables to construct a model with high predictive accuracy. We expect that the prediction of variation will be useful for improving the practical development of materials and medicines, such as artificial bones, for which stable effects are required, regardless of the individual. 10.1002/ansa.70021 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/ansa.70021 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ansa_70021 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko Analytical Science Advances Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results Yuta Sakai Shota Horikawa Mamoru Aizawa Hiromasa Kaneko Analytical Science Advances ABSTRACTThe proportion of older people in the world's total population is expected to increase. Bone diseases are more prevalent in older people; therefore, the number of patients with such diseases is expected to increase worldwide. Artificial bone is a biomaterial used in the treatment of bone diseases. Artificial bones with high bone formation rates are desired; however, the results of artificial bone implantation vary. There are also ethical issues associated with animal experiments. Our purpose in this study is to predict the variation in bone formation rates. We created multiple sub‐datasets and constructed a machine learning model to predict the variation in bone formation rates by considering the results of multiple measurements. We also propose a metric, Jensen–Shannon (JS) divergence, to evaluate the accuracy of the model for predicting variation. We tested the validity of JS divergence by comparing combinations of explanatory variables. Additionally, we found an optimal combination of explanatory variables to construct a model with high predictive accuracy. We expect that the prediction of variation will be useful for improving the practical development of materials and medicines, such as artificial bones, for which stable effects are required, regardless of the individual. 10.1002/ansa.70021 http://creativecommons.org/licenses/by/4.0/ |
| title | Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results |
| topic | Analytical Science Advances |
| url | https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/ansa.70021 |