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Abstract：
We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and improving patient care. Data from 2286 geriatric patients who underwent surgery and were admitted to the ICU of Dongyang People\'s Hospital between 2012 and 2021 were retrospectively analyzed. Integrated algorithms were developed, and four machine-learning algorithms were used. Selected characteristics included common demographic data, biochemical indicators, and vital signs. Eight key variables were selected using the Least Absolute Shrinkage and Selection Operator and Random Forest Algorithm. Thrombocytopenia occurred in 18.2% of postoperative geriatric patients, with a higher mortality rate. The C5.0 model showed the best performance, with an area under the receiver operating characteristic curve close to 0.85, along with unparalleled accuracy, precision, specificity, recall, and balanced accuracy scores of 0.88, 0.98, 0.89, 0.98, and 0.85, respectively. The support vector machine model excelled at predictively assessing thrombocytopenia severity, demonstrating an accuracy rate of 0.80 in the MIMIC database. Thus, our machine learning-based models have considerable potential in effectively predicting the risk and severity of postoperative thrombocytopenia in geriatric ICU patients for better clinical decision-making and patient care.
摘要：
我们开发了一种可解释的机器学习算法，可以前瞻性地预测老年危重病人在重症监护病房（ICU）期间的血小板减少症风险。最终帮助临床决策和改善患者护理。回顾性分析2012年至2021年东阳市人民医院ICU收治的2286例老年患者的手术资料。开发了集成算法，并使用了四种机器学习算法。选定的特征包括常见的人口统计数据，生化指标,和生命体征。使用最小绝对收缩和选择算子和随机森林算法选择了八个关键变量。18.2%的老年患者术后发生血小板减少,死亡率较高。C5.0模型表现出最好的性能，接收器工作特性曲线下的面积接近0.85，具有无与伦比的精度，精度,特异性，召回，和平衡准确度得分分别为0.88、0.98、0.89、0.98和0.85。支持向量机模型在预测血小板减少严重程度方面表现优异，在MIMIC数据库中显示0.80的准确率。因此，我们的基于机器学习的模型在有效预测老年ICU患者术后血小板减少的风险和严重程度方面具有相当大的潜力,以便更好的临床决策和患者护理.
