Allogeneic hematopoietic cell transplantation (HCT) effectively treats high-risk hematologic diseases but can entail HCT-specific complications, which may be minimized by appropriate patient management, supported by accurate, individual risk estimation. However, almost all HCT risk scores are limited to a single risk assessment before HCT without incorporation of additional data. We developed machine learning models that integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These gradient boosting machine models provide well-calibrated, time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision–recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both models were successfully validated in a prospective, non-interventional study and performed on par with expert hematologists in a pilot comparison.

中文翻译：

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使用机器学习对异基因造血细胞移植后死亡率和巨细胞病毒再激活的时间依赖性预测

异基因造血细胞移植 (HCT) 可有效治疗高危血液疾病，但可能会导致 HCT 特异性并发症，通过适当的患者管理和准确的个体风险评估可将其最小化。然而，几乎所有 HCT 风险评分都仅限于 HCT 之前的单一风险评估，而没有纳入额外的数据。我们开发了机器学习模型，该模型整合了基线患者数据和时间相关的实验室测量结果，以在每个患者的多个时间点分别预测 HCT 后的死亡率和巨细胞病毒 (CMV) 再激活。这些梯度提升机器模型提供了经过良好校准的、与时间相关的风险预测，并在 0.92 和 0.83 的接收者操作特征下实现了区域，在 0.58 和 0 的精确召回曲线下实现了区域。62 分别用于预测 21 天时间窗口内的死亡率和 CMV 再激活。两种模型都在一项前瞻性、非干预性研究中成功验证，并在试点比较中与专家血液学家相媲美。