Title
Erythropoiesis Stimulating Agent Recommendation Model Using Recurrent Neural Networks For Patient With Kidney Failure With Replacement Therapy
Abstract
In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these pa-tients is a challenging problem because of the complexities of the underlying diseases and heterogeneous re-sponses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 mu g (ESAs dose), respectively. Compared to the results in the real -world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 mu g/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104718
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Kidney failure with replacement therapy, Anemia, Erythropoiesis stimulating agent, Recurrent neural networks
Journal
137
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
12
Name
Order
Citations
PageRank
Hae-Ryong Yun100.34
Gyubok Lee200.34
Myeong Jun Jeon300.34
Hyung-Woo Kim441.88
Young Su Joo500.34
Hyoungnae Kim600.34
Tae Ik Chang700.34
Jung Tak Park800.34
Seung Hyeok Han900.34
Shin-Wook Kang1001.01
Wooju Kim1125629.73
Tae-Hyun Yoo1200.34