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A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
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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.

BMC Medical Informatics and Decision Making
|October 18, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a predictive model for neonatal necrotizing enterocolitis (NEC) using long short-term memory networks (LSTM) and focal loss (FL). The model accurately predicts the need for surgical intervention in infants with NEC, enabling earlier treatment.

Keywords:
Auxiliary diagnosisDeep learningLong short-term memory networkNeonatal necrotizing enterocolitisPredictive surgery

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Area of Science:

  • Neonatalogy
  • Artificial Intelligence in Medicine
  • Surgical Prediction

Background:

  • Neonatal necrotizing enterocolitis (NEC) presents challenges in determining optimal surgical timing.
  • Early identification of infants at risk for severe NEC (Bell IIB+) is crucial for timely intervention.

Purpose of the Study:

  • To develop a predictive model utilizing Long Short-Term Memory (LSTM) networks with Focal Loss (FL).
  • To identify infants at high risk for developing Bell IIB+ NEC and provide early surgical warnings.

Main Methods:

  • Utilized data from 791 neonates diagnosed with NEC, including 35 features.
  • Employed a fivefold cross-validation approach for training and testing the LSTM model.
  • Applied Focal Loss (FL) to address class imbalance and capture temporal relationships.

Main Results:

  • The model achieved high performance in predicting surgical risk one day in advance (Precision: 0.913, Recall: 0.841, F1: 0.874).
  • Predictions made two days in advance also showed strong performance (Precision: 0.905, Recall: 0.815, F1: 0.857).
  • The model demonstrated high average precision (AP) for both 1-day (0.917) and 2-day (0.905) predictions.

Conclusions:

  • The LSTM model with FL accurately forecasts the need for surgical intervention in NEC patients 1-2 days in advance.
  • This predictive capability can significantly improve clinical decision-making and enhance infant outcomes.
  • Timely surgical warnings facilitated by the model promise better management of severe NEC cases.