A time series algorithm to predict surgery in neonatal necrotizing enterocolitis

Abstract

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.

Keywords: Auxiliary diagnosis; Deep learning; Long short-term memory network; Neonatal necrotizing enterocolitis; Predictive surgery.

Fig. 1

Standardized exclusion criteria: the flowchart demonstrates the selection of research cases based on inclusion and exclusion criteria

Fig. 2

The data missing rate at different sampling intervals: Shows the data missing situation of 35 features at sampling intervals of 1 day, 2 days, and 3 days, respectively

Fig. 3

Model performance at different sampling intervals and missing data rates

Fig. 4

Flowchart of the model prediction process. After preprocessing the raw data, selecting features, dividing into training and testing sets, and serializing, the data is input into the LSTM model with FL as the loss function, resulting in predictions

Fig. 5

The performance of models with different settings: As γ gradually increases from 1 to 8, the overall performance of the model exhibits a fluctuating downward trend. Compared to the Cross-Entropy (CE) loss function, the focal loss (FL) function demonstrates superior performance

Fig. 6

Surgical Prediction PR Curve for 1 Day and 2 Days

Fig. 7

The Average Performance in Predicting Surgery 1 or 2 Days in Advance. The left plot illustrates the performance of predicting NEC surgery 1 day in advance, while the right plot displays the performance of predicting NEC surgery 2 days in advance

Fig. 8

Predict Confusion Matrix: 1 Day in Advance - Confusion matrix for predicting NEC surgery one day in advance

Fig. 9

Predict Confusion Matrix: 2 Day in Advance - Confusion matrix for predicting NEC surgery two days in advance

Fig. 10

Clinical Feature Contribution to the Model - Relative contributions of various features in predicting NEC surgery. A higher feature score indicates greater importance of the variable to the model