Background Osteoporosis is a metabolic bone disorder characterised by decreased bone mineral density and mass. Due to its asymptomatic nature, it often remains undiagnosed and untreated until a fracture occurs. Traditionally, treatment decisions for osteoporosis are based on clinical appropriateness while balancing the treatment's risks and benefits. Machine learning (ML) is revolutionising healthcare domains through pattern recognition of previously “unseen” observations. Presently, its application in osteoporosis is limited to early diagnosis. More research is needed to examine its role in guiding osteoporosis treatment. This study aims to identify new predictive attributes for osteoporosis treatment using ML techniques on data from the Irish Hip Fracture Database (IHFD). Methods Datasets from January to March 2023 in University Hospital Waterford were sourced from the IHFD. Osteoporosis treatment decisions were obtained from discharge letters. Preliminary data cleaning was performed in Excel with zero-variance and near-zero predictor. Attributes excluded. The dataset was entered into the WEKA 3.8.6 environment for ML processing. Results The initial dataset containing 141 instances and 32 attributes was refined using the Correlation Feature Selection and Ranker Search Method, identifying key osteoporosis treatment predictors. The highest correlation attributes are pre-fracture total score, pre-fracture indoor score, and age. Moderately positive correlations are discharge destination, pre-fracture outdoor and shopping score, ASA grade, Length-of-stay, admission code, Admission 4AT score, Frailty scale, and fracture type. The implemented J48 Tree ML-trained model revealed Correctly Classified Instances and Incorrectly Classified Instances of 98.24% and 1.7%, respectively, indicating a high prediction accuracy rate. Conclusion This study demonstrates the potential of ML in enhancing osteoporosis treatment decision-making by leveraging datasets from the IHFD. Integrating ML algorithms with traditional approaches can provide a comprehensive, nuanced and personal approach to osteoporosis treatment and patient care. The study opens avenues for future research in applying big data and advanced analytics in healthcare, underscoring the evolving landscape of medical decision-making.

中文翻译：

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Bone-A-Fide 突破：机器学习使用爱尔兰髋部骨折数据库破解骨质疏松症治疗的密码


背景 骨质疏松症是一种代谢性骨病，其特征是骨密度和质量降低。由于其无症状性质，在骨折发生之前，它通常未被诊断和治疗。传统上，骨质疏松症的治疗决策基于临床适宜性，同时平衡治疗的风险和益处。机器学习 （ML） 通过对以前“看不见”的观察结果进行模式识别，正在彻底改变医疗保健领域。目前，它在骨质疏松症中的应用仅限于早期诊断。需要更多的研究来检验其在指导骨质疏松症治疗中的作用。本研究旨在根据爱尔兰髋部骨折数据库 （IHFD） 的数据使用 ML 技术确定骨质疏松症治疗的新预测属性。方法 2023 年 1 月至 3 月沃特福德大学医院的数据集来自 IHFD。骨质疏松症治疗决策从出院信中获得。在 Excel 中以零方差和接近零的预测变量进行初步数据清理。排除的属性。数据集被输入到 WEKA 3.8.6 环境中进行 ML 处理。结果 包含 141 个实例和 32 个属性的初始数据集使用 Correlation Feature Selection 和 Ranker Search Method 进行细化，确定了关键的骨质疏松症治疗预测因子。相关性最高的属性是骨折前总分、骨折前室内评分和年龄。出院目的地、骨折前户外和购物评分、ASA 等级、住院时间、入院代码、入院 4AT 评分、虚弱量表和骨折类型呈中等正相关。实施的 J48 Tree ML 训练模型揭示了 98.24% 和 1 的正确分类实例和错误分类实例。7%，表示预测准确率高。结论 本研究通过利用 IHFD 的数据集证明了 ML 在增强骨质疏松症治疗决策方面的潜力。将 ML 算法与传统方法相结合，可以为骨质疏松症治疗和患者护理提供全面、细致和个性化的方法。该研究为未来在医疗保健中应用大数据和高级分析的研究开辟了途径，强调了医疗决策不断发展的前景。