Serum Sodium Fluctuation Prediction among ICU Patients Using Neural Network Algorithm: Analysis of the MIMIC-IV Database

Abstract

Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit (ICU), which may lead to physiological disorders of many organs. The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’ experience. This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients. The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care (MIMIC)-IV. The time point of serum sodium detection was selected from the ICU clinical records, and the ICU records of 25 risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis. A prediction model of serum sodium value within 48 h was established using a feedforward neural network, and compared with previous methods. Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h, and has better prediction effect than the serum sodium formula and other machine learning models.

Keywords

serum sodium / structured electronic medical record / hypernatremia / hyponatremia / neural network / machine learning