Transfer learning-enabled outcome prediction for guiding CRRT treatment of the pediatric patients with sepsis

Abstract

Continuous renal replacement therapy (CRRT) is a life-saving procedure for sepsis but the benefit of CRRT varies and prediction of clinical outcomes is valuable in efficient treatment planning. This study aimed to use machine learning (ML) models trained using MIMIC III data for identifying sepsis patients who would benefit from CRRT. We first selected patients with sepsis and CRRT in the ICU setting and their gender, and an array of routine lab results were included as features to train machine learning models using 30-day mortality as the primary outcome. A total of 4161 patients were included for analysis, among whom there were 1342 deaths within 30 days. Without data augmentation, extreme gradient boosting (XGBoost) showed an accuracy of 64.2% with AUC-ROC of 0.61. Data augmentation using a conditional generative adversarial neural network (c-GAN) resulted in a significantly improved accuracy (82%) and ROC-AUC (0.78%). To enable prediction on pediatric patients, we adopted transfer learning approaches, where the weights of all but the last hidden layer were fixed, followed by fine-tuning of the weights of the last hidden layer using pediatric data of 200 patients as the inputs. A significant improvement was observed using the transfer learning approach (AUCROC = 0.76) compared to direct training on the pediatric cohort (AUCROC = 0.62). Through this transfer-learning-facilitated patient outcome prediction, our study showed that ML can aid in clinical decision-making by predicting patient responses to CRRT for managing pediatric sepsis.

Keywords: CRRT; Clinical validation; MIMIC III; Machine-learning; Newborn; Sepsis.

Fig. 1

(A) Schematic illustration of c-GAN for data augmentation. (B) Comparison of features of synthetic and real data. Data were represented as mean + 95%CI

Fig. 2

Rationale for transfer learning. Model pre-trained on large and augmented adult dataset was applied to a different domain, i.e., pediatric patients, where the weights of the last hidden layer (labeled in blue) were fine-tuned

Fig. 3

Feature importance scores of top 10 features based on SHAP on augmented dataset. Scr: serum creatinine; BUN: blood urea nitrogen; K+: potassium; Na+: Sodium; WBC: white blood cell; Hb: hemoglobin; CRP: C-reactive protein; PLT: platelet count; pO2: oxygen pressure of blood