Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to provide suggestions when some strict requirements are not met (such as having a three month history prior to the prediction). Here, recurrent neural networks (RNNs) are used to model whole patient histories, providing predictions at every time step since the very first day. Furthermore, an unprecedented amount of data (∼110,000 patients from many different medical centers in twelve countries, without exclusion criteria) was used to train it, thus allowing it to generalize for every single patient. The resulting model outperforms state-of-the-art Hemoglobin prediction, providing excellent results even when tested on a prospective dataset. Simultaneously, it allows to bring the benefits of algorithmic anemia control to a very large group of patients.

红细胞生成刺激剂 (ESA) 已成为终末期肾病 (ESRD) 患者的标准贫血管理工具。然而，由于对 ESA 给药的反应存在巨大的患者间和患者内变异性，剂量优化构成了一项极具挑战性的任务。当前基于数据的贫血控制方法侧重于学习准确的血红蛋白预测模型，这些模型以后可用于测试竞争性治疗选择并选择最佳治疗方案。尽管这些方法在实践中被证明是有效的，但存在一些本文打算解决的缺点。也就是说，他们仅限于一小群患者，即使这样，当一些严格的要求没有得到满足（例如在预测之前有三个月的历史）时，他们也无法提供建议。这里，循环神经网络 (RNN) 用于对整个患者病史进行建模，从第一天起在每个时间步提供预测。此外，史无前例的数据量（来自 12 个国家的许多不同医疗中心的约 110,000 名患者，没有排除标准）用于训练它，从而使其能够推广到每个患者。由此产生的模型优于最先进的血红蛋白预测，即使在前瞻性数据集上进行测试时也能提供出色的结果。同时，它可以为大量患者带来算法贫血控制的好处。来自 12 个国家/地区的许多不同医疗中心的 000 名患者（没有排除标准）用于训练它，从而使其能够推广到每个患者。由此产生的模型优于最先进的血红蛋白预测，即使在前瞻性数据集上进行测试时也能提供出色的结果。同时，它可以为大量患者带来算法贫血控制的好处。来自 12 个国家/地区的许多不同医疗中心的 000 名患者（没有排除标准）用于训练它，从而使其能够推广到每个患者。由此产生的模型优于最先进的血红蛋白预测，即使在前瞻性数据集上进行测试时也能提供出色的结果。同时，它可以为大量患者带来算法贫血控制的好处。