Department of Information Systems, Freie Universität Berlin, Germany.
Institute of Medical Informatics, Charité - Universitätsmedizin, Germany.
Stud Health Technol Inform. 2022 May 25;294:575-576. doi: 10.3233/SHTI220530.
Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We developed a boosting algorithm to predict both recurrent falls and the severity of fall injuries. The model was trained on a dataset including extensive information on fall events of patients who had been admitted to Charité - Universitätsmedizin Berlin between August 2016 and July 2020. The data were recorded according to the German expert standard for fall documentation. Predictive power scores were calculated to define optimal feature sets. With an accuracy of 74% for recurrent falls and 86% for injury severity, boosting demonstrated the best overall predictive performance of all models assessed. Given that our data contain initially rated risk scores, our results demonstrate that well trained ML algorithms possibly provide tools to substantially reduce fall risks in clinical care settings.
标准化的跌倒风险评分在临床环境中并不能可靠地预测跌倒。机器学习有潜力提高此类预测的准确性，从而可能极大地改善高跌倒风险患者的护理。我们开发了一种提升算法来预测复发性跌倒和跌倒伤害的严重程度。该模型是在一个数据集上训练的，该数据集包含了 2016 年 8 月至 2020 年 7 月期间在柏林夏洛蒂医科大学住院的患者跌倒事件的详细信息。数据是根据德国跌倒文档专家标准记录的。预测能力评分被用来定义最佳特征集。提升算法在复发性跌倒方面的准确率为 74%，在伤害严重程度方面的准确率为 86%，表现出所有评估模型中最佳的整体预测性能。鉴于我们的数据包含最初评定的风险评分，我们的结果表明，经过良好训练的机器学习算法可能为在临床护理环境中大幅降低跌倒风险提供工具。
