#5745. A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data

July 2026publication date
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Journal’s subject area:
Computer Science Applications;
Health Informatics;
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Abstract:
Background: Most end-stage renal disease patients rely on hemodialysis (HD) to maintain their life, and they face a serious financial burden and high risk of mortality. Due to the current situation of the health care system in China, a large number of patients on HD are lost to follow-up, making the identification of patients with high mortality risks an intractable problem. Objective: This paper aims to propose a maintenance HD mortality prediction approach using longitudinal HD data under the situation of data imbalance caused by follow-up losses. Methods: A long short-term memory autoencoder (LSTM AE) based model is proposed to capture the physical condition changes of HD patients and distinguish between surviving and nonsurviving patients. The approach adopts anomaly detection theory, using only the surviving samples in the model training and identifying dead samples based on autoencoder reconstruction errors.
Keywords:
Anomaly detection; Electronic health records; Hemodialysis; LSTM autoencoders; Mortality prediction

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