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Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset

Andrei-Alexandru Bunea, Ovidiu Ghibea, Dan-Matei Popovici, Ion Daniel, Octavian Andronic

Abstract

We develop and analyze explainable machine learning (ML) models for sepsis outcome prediction using a novel Electronic Health Record (EHR) dataset from 12,286 hospitalizations at a large emergency hospital in Romania. The dataset includes demographics, International Classification of Diseases (ICD-10) diagnostics, and 600 types of laboratory tests. This study aims to identify clinically strong predictors while achieving state-of-the-art results across three classification tasks: (1)deceased vs. discharged, (2)deceased vs. recovered, and (3)recovered vs. ameliorated. We trained five ML models to capture complex distributions while preserving clinical interpretability. Experiments explored the trade-off between feature richness and patient coverage, using subsets of the 10--50 most frequent laboratory tests. Model performance was evaluated using accuracy and area under the curve (AUC), and explainability was assessed using SHapley Additive exPlanations (SHAP). The highest performance was obtained for the deceased vs. recovered case study (AUC=0.983, accuracy=0.93). SHAP analysis identified several strong predictors such as cardiovascular comorbidities, urea levels, aspartate aminotransferase, platelet count, and eosinophil percentage. Eosinopenia emerged as a top predictor, highlighting its value as an underutilized marker that is not included in current assessment standards, while the high performance suggests the applicability of these models in clinical settings.

Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset

Abstract

We develop and analyze explainable machine learning (ML) models for sepsis outcome prediction using a novel Electronic Health Record (EHR) dataset from 12,286 hospitalizations at a large emergency hospital in Romania. The dataset includes demographics, International Classification of Diseases (ICD-10) diagnostics, and 600 types of laboratory tests. This study aims to identify clinically strong predictors while achieving state-of-the-art results across three classification tasks: (1)deceased vs. discharged, (2)deceased vs. recovered, and (3)recovered vs. ameliorated. We trained five ML models to capture complex distributions while preserving clinical interpretability. Experiments explored the trade-off between feature richness and patient coverage, using subsets of the 10--50 most frequent laboratory tests. Model performance was evaluated using accuracy and area under the curve (AUC), and explainability was assessed using SHapley Additive exPlanations (SHAP). The highest performance was obtained for the deceased vs. recovered case study (AUC=0.983, accuracy=0.93). SHAP analysis identified several strong predictors such as cardiovascular comorbidities, urea levels, aspartate aminotransferase, platelet count, and eosinophil percentage. Eosinopenia emerged as a top predictor, highlighting its value as an underutilized marker that is not included in current assessment standards, while the high performance suggests the applicability of these models in clinical settings.

Paper Structure

This paper contains 11 sections, 11 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Dataset
  4. Data Curation and Preprocessing
  5. Classification Scenarios and Experimental Framework
  6. Machine Learning Models
  7. Results
  8. Model Performance
  9. SHAP Interpretation and Clinical Significance
  10. Discussion
  11. Conclusion

Figures (11)

  • Figure 1: Descriptive statistics of the dataset: (a) age distribution, (b) sex distribution, and (c) hospitalization duration.
  • Figure 2: AUC comparison of individual classifiers for Task 1---Deceased vs. Discharged. Diagnostic feature sets ("diag") yield higher performance than non-diagnostic ones ("nodiag"), with Gradient Boosting variants achieving the best overall results.
  • Figure 3: Model performance comparison (AUC) for Deceased vs. Recovered patients across diagnostic subsets.
  • Figure 4: Model performance comparison (AUC) for Recovered vs. Ameliorated patients across diagnostic subsets. Histogram-based Gradient Boosting models achieve the highest AUC values across all subsets.
  • Figure 5: SHAP summary plot for Deceased vs. Discharged top classifier (HistGB model trained on the top-40 diagnostic subset). Features are ranked by impact on mortality prediction.
  • ...and 6 more figures