Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.

中文翻译：

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机器学习可以识别出高感染风险的新诊断CLL患者。

由于免疫功能低下和细胞毒性CLL治疗，感染已成为慢性淋巴细胞性白血病（CLL）患者发病和死亡的主要原因。但是，缺少用于感染的预测模型。在这项工作中，我们开发了CLL治疗感染模型（CLL-TIM），该模型可以识别出在诊断后2年内有感染风险或CLL治疗风险的患者，这在内部和外部队列中均得到了验证。CLL-TIM是一种集成算法，由28种机器学习算法组成，基于来自4149名CLL患者的数据。该模型能够处理异构数据，包括真实环境中预期的高丢失率数据，准确率达72％，召回率达75％。为了解决有关在诊所中使用复杂机器学习算法的问题，