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Updated: Jun 10, 2025
A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
A time series algorithm to predict surgery in neonatal necrotizing enterocolitis
Cheng Cui1, Ling Qiu2, Ling Li3
1Neonatal Diagnosis and Treatment Center of Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, 400014, China.
Background:
Determining the optimal timing of surgical intervention for Neonatal necrotizing enterocolitis (NEC) poses significant challenges. This study develops a predictive model using the long short-term memory network (LSTM) with a focal loss (FL) to identify infants at risk of developing Bell IIB + NEC early and issue timely surgical warnings.
Methods:
Data from 791 neonates diagnosed with NEC are gathered from the Neonatal Intensive Care Unit (NICU), encompassing 35 selected features. Infants are categorized into those requiring surgical intervention (n = 257) and those managed medically (n = 534) based on the Mod-Bell criteria. A fivefold cross-validation approach is employed for training and testing. The LSTM algorithm is utilized to capture and utilize temporal relationships in the dataset, with FL employed as a loss function to address class imbalance. Model performance metrics include precision, recall, F1 score, and average precision (AP).
Results:
The model tested on a real dataset demonstrated high performance. Predicting surgical risk 1 day in advance achieved precision (0.913 ± 0.034), recall (0.841 ± 0.053), F1 score (0.874 ± 0.029), and AP (0.917 ± 0.025). The 2-days-in-advance predictions yielded (0.905 ± 0.036), recall (0.815 ± 0.057), F1 score (0.857 ± 0.035), and AP (0.905 ± 0.029).
Conclusion:
The LSTM model with FL exhibits high precision and recall in forecasting the need for surgical intervention 1 or 2 days ahead. This predictive capability holds promise for enhancing infants' outcomes by facilitating timely clinical decisions.
