This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost model was trained utilizing 80\% of the train dataset, encompassing 107 features (including the original and derived features). Subsequently, the model was evaluated on the remaining 20\% of the test data. The model was validated on prospective data that was entirely unseen during the training phase. To assess the model's performance at the individual patient level and timeliness of the prediction, a normalized utility score was employed, a widely recognized scoring methodology for sepsis detection, as outlined in the PhysioNet Sepsis Challenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag Rate were also devised. The model achieved a normalized utility score of 0.494 on test data and 0.378 on prospective data at threshold 0.3. The F1 scores were 80.8\% and 67.1\% respectively for the test data and the prospective data for the same threshold, highlighting its potential to be integrated into clinical decision-making processes effectively. These results bear testament to the model's robust predictive capabilities and its potential to substantially impact clinical decision-making processes.

翻译：本研究提出利用机器学习模型，基于美国纽约布朗克斯区蒙特菲奥雷医疗中心的去标识化临床数据，预测脓毒症的早期发病。采用监督学习方法，利用包含107个特征（含原始特征与衍生特征）的训练数据集的80%训练XGBoost模型。随后，该模型在剩余20%的测试数据上进行评估，并在训练阶段完全未见的前瞻性数据上进行了验证。为评估模型在个体患者层面的性能及预测时效性，采用PhysioNet脓毒症挑战赛论文中提出的标准化效用评分（一种广泛认可的脓毒症检测评分方法），并设计了F1分数、灵敏度、特异度及标记率等指标。在阈值为0.3时，模型在测试数据上取得0.494的标准化效用评分，在前瞻性数据上取得0.378；同一阈值下，测试数据与前瞻性数据的F1分数分别为80.8%和67.1%，凸显该模型有效融入临床决策流程的潜力。这些结果充分验证了模型强大的预测能力及其对临床决策过程产生实质性影响的潜力。