2022
DOI: 10.1016/j.cmpb.2022.107028
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Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis

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Cited by 15 publications
(11 citation statements)
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References 30 publications
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“…Machine learning techniques have been implemented for drug discovery. Lin et al [ 24 ] compared four machine learning models (logistic regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) for personalized treatment of osteoporosis. For testing the generalizability of the models, the main analysis (196 patients) and subgroup analysis (154 patients) were conducted.…”
Section: Related Workmentioning
confidence: 99%
“…Machine learning techniques have been implemented for drug discovery. Lin et al [ 24 ] compared four machine learning models (logistic regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) for personalized treatment of osteoporosis. For testing the generalizability of the models, the main analysis (196 patients) and subgroup analysis (154 patients) were conducted.…”
Section: Related Workmentioning
confidence: 99%
“…Lin et al [29] conducted a retrospective study between 2011 to 2018 at Wan Fang Hospital, in Taipei, Taiwan. Their process investigated 196 patients as a whole.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Numerous studies have employed a variety of ML techniques such as logistic regression, XGBoost, random forest, K-nearest neighbor, support vector machine, decision trees, and neural networks. These methods address various facets of osteoporosis from risk prediction and early detection to diagnosis, treatment, and management [ 19 23 ].…”
Section: Introductionmentioning
confidence: 99%