The most severe form of kidney disease, End-Stage Renal Disease (ESRD) is treated with various forms of dialysis – artificial blood cleansing. Dialysis patients suffer many health burdens including high mortality and hospitalization rates, and symptomatic anemia: a low red blood cell count as indicated by a low hemoglobin (Hgb) level. ESRD-induced anemia is treated, with variable patient response, by erythropoiesis stimulating agents (ESAs): expensive injectable medications typically administered during dialysis sessions. The dosing protocol is typically a population level protocol based on original clinical trials, the use of which often results in Hgb cycling. This cycling phenomenon occurs primarily due to the mismatch in the time between dosing decisions and the time it takes for the effects of a dosing change to be fully realized. In this paper we develop a recurrent neural network approach that uses historic data together with future ESA and iron dosing data to predict the 1, 2, and 3 month Hgb levels of patients with ESRD-induced anemia. The results of extensive experimentation indicate that this approach generates predictions that are clinically relevant: the mean absolute error of the predictions is comparable to estimates of the intra-individual variability of the laboratory test for Hgb.

肾病的最严重形式——终末期肾病 (ESRD) 通过各种形式的透析进行治疗——人工血液净化。透析患者承受着许多健康负担，包括高死亡率和住院率，以及有症状的贫血：低血红蛋白 (Hgb) 水平表明红细胞计数低。通过红细胞生成刺激剂 (ESA) 治疗 ESRD 引起的贫血，患者的反应各不相同：通常在透析过程中使用的昂贵的注射药物。给药方案通常是基于原始临床试验的群体水平方案，其使用通常会导致 Hgb 循环。这种循环现象的发生主要是由于给药决定之间的时间与完全实现给药变化效果所需的时间不匹配。在本文中，我们开发了一种循环神经网络方法，该方法使用历史数据以及未来的 ESA 和铁剂量数据来预测 ESRD 引起的贫血患者的 1、2 和 3 个月 Hgb 水平。大量实验的结果表明，这种方法生成的预测具有临床相关性：预测的平均绝对误差与 Hgb 实验室测试的个体内变异性估计值相当。