World Health Organization (WHO) guidelines recommend early identification of patients who have emergency features for early medical intervention with the aim of reducing child mortality and morbidity. Prognostic models have been developed to be used in clinical setups, but their performance in external validations has been dismal. These poor performances have been attributed to suboptimal statistical methods used for derivation of these scores. Methods: The Bayesian finite mixture model was used to succinctly identify subpopulations in a population of 47,596 patients from different geographical regions. Mixed models were used to derive a final prognostic model taking into account subgroups of the population. Clinically relevant yet routinely available prognostic factors were used in model development. Results: Amongst the 23 risk factors used, the AVPU scale which measures unconsciousness was the strongest predictor of mortality with odds of (AOR=2.94, 95% CI= 2.57 - 3.36). Oedema (AOR= 2.66, 95% CI= 2.18 - 3.24), pallor (AOR=2.09, 95% CI= 1.86 - 2.36) and the presence of >= 3 severe comorbidities (AOR=2.19, 95% CI= 1.73 - 2.74) were also associated with an increased risk of death. Conclusion: Given that patient are not alike, a statistical methodology that clusters patients into homogeneous subpopulations should be used to account for the inherent variability in the medical patients. Computational methodology such as mixture models should be used to identify inherent subpopulations that underlie the population of medical patients under study. Limitation: The use of diagnostic episodes as one of predictors in the model was based on the clinician’s impression (not a laboratory test) thus the possibility of false positives could not be ruled out.