A machine learning approach to predicting inpatient mortality among pediatric acute gastroenteritis patients in Kenya

Mortality prediction scores for children admitted with diarrhea are unavailable, early identification of at-risk patients for proper management remains a challenge. This study utilizes machine learning (ML) to develop a highly sensitive model for timelier identification of at-risk children admitted with acute gastroenteritis (AGE) for better management.

We used seven ML algorithms to build prognostic models for the prediction of mortality using de-identified data collected from children aged <5 years hospitalized with AGE at Siaya County Referral Hospital (SCRH), Kenya, between 2010 through 2020. Potential predictors included demographic, medical history, and clinical examination data collected at admission to hospital. We conducted split-sampling and employed tenfold cross-validation in the model development. We evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) for each of the models.

During the study period, 12 546 children aged <5 years admitted at SCRH were enrolled in the inpatient disease surveillance, of whom 2271 (18.1%) had AGE and 164 (7.2%) subsequently died. The following features were identified as predictors of mortality in decreasing order: AVPU scale, Vesikari score, dehydration, sunken eyes, skin pinch, maximum number of vomits, unconsciousness, wasting, vomiting, pulse, fever, sunken fontanelle, restless, nasal flaring, diarrhea days, stridor, <90% oxygen saturation, chest indrawing, malaria, and stunting. The sensitivity ranged from 46.3%-78.0% across models, while the specificity and AUC ranged from 71.7% to 78.7% and 56.5%-82.6%, respectively. The random forest model emerged as the champion model achieving 78.0%, 76.6%, 20.6%, 97.8%, and 82.6% for sensitivity, specificity, PPV, NPV, and AUC, respectively.

This study demonstrates promising predictive performance of the proposed algorithm for identifying patients at risk of mortality in resource-limited settings. However, further validation in real-world clinical settings is needed to assess its feasibility and potential impact on patient outcomes.