Lin Yi-Ting, Chu Chao-Yu, Hung Kuo-Sheng, Lu Chi-Hua, Bednarczyk Edward M, Chen Hsiang-Yin
Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, No. 250 Wuxing St., Xinyi District, Taipei 11031, Taiwan.
Department of Neurosurgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Comput Methods Programs Biomed. 2022 Oct;225:107028. doi: 10.1016/j.cmpb.2022.107028. Epub 2022 Jul 21.
The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of osteoporosis. The model performance on outcome prediction was compared between four machine learning algorithms.
Retrospective, electronic clinical data for patients with suspected or confirmed osteoporosis treated at Wan Fang Hospital between 2011 to 2018 were used as inputs for building the following predictive machine learning models,i.e., artificial neural network (ANN), random forest (RF), support vector machine (SVM) and logistic regression (LR) models. The predicted outcome was defined as an increase/decrease in T-score after treatment. A genetic algorithm was employed to select relevant variables as input features for each model; the leave-one-out method was applied for model building and internal validation. The model with best performance was selected by a separate set of testing. Area under the receiver operating characteristic curve, accuracy, precision, sensitivity and F1 score were calculated to evaluate model performance. Main analysis for all the patients with subclinical or confirmed osteoporosis and subgroup analysis for the patients with confirmed osteoporosis (T score < -2.5) were carried out in this study.
A genetic algorithm was employed to select 12 to 18 features from all 33 variables for the four models. No difference was found in accuracy (ANN, 71.7%; LR, 70.0%; RF, 75.0%; SVM, 66.7%), precision (ANN, 80.0%; LR, 59.3%; RF, 70.0%; SVM, 63.6%), and AUC (ANN, 0.709; LR, 0.731; RF, 0.719; SVM, 0.702) among the ANN, LR, RF and SVM models. Main analysis in performance revealed significant recall in the LR model, as compared to ANN and SVM model; while subgroup revealed significant recall in ANN model, compared to LR and SVM model.
Machine learning-based models hold potential in forecasting the outcomes of treatment for osteoporosis via early initiation of first-line therapy for patients with subclinical disease; or a switch to second-line treatment for patients with a high risk of impending treatment failure. This convenient approach can assist clinicians in adjusting treatment tailored to individual patient for prevention of disease progression or ineffective therapy.
本研究的具体目的是开发机器学习模型，作为骨质疏松症个性化治疗的临床方法。比较了四种机器学习算法在结局预测方面的模型性能。
回顾性分析2011年至2018年在万方医院接受疑似或确诊骨质疏松症治疗的患者的电子临床数据，将其作为构建以下预测性机器学习模型的输入，即人工神经网络（ANN）、随机森林（RF）、支持向量机（SVM）和逻辑回归（LR）模型。预测结局定义为治疗后T值的升高/降低。采用遗传算法为每个模型选择相关变量作为输入特征；采用留一法进行模型构建和内部验证。通过另一组测试选择性能最佳的模型。计算受试者工作特征曲线下面积、准确性、精确性、敏感性和F1分数来评估模型性能。本研究对所有亚临床或确诊骨质疏松症患者进行了主要分析，并对确诊骨质疏松症（T值＜-2.5）患者进行了亚组分析。
采用遗传算法从所有33个变量中为四个模型选择12至18个特征。ANN、LR、RF和SVM模型在准确性（ANN，71.7%；LR，70.0%；RF，75.0%；SVM，66.7%）、精确性（ANN，80.0%；LR，59.3%；RF，70.0%；SVM，63.6%）和AUC（ANN，0.709；LR，0.731；RF，0.719；SVM，0.702）方面未发现差异。性能的主要分析显示，与ANN和SVM模型相比，LR模型的召回率显著；而亚组分析显示，与LR和SVM模型相比，ANN模型的召回率显著。
基于机器学习的模型在通过为亚临床疾病患者早期启动一线治疗或为即将治疗失败风险高的患者改用二线治疗来预测骨质疏松症治疗结局方面具有潜力。这种便捷的方法可以帮助临床医生调整针对个体患者的治疗方案，以预防疾病进展或无效治疗。
