The application of machine learning models in a resource-constrained environment

Machine learning models (MLMs) used to influence surgical decision making often require large and complex datasets upon which to train. However, there is a paucity of literature pertaining to the ability to apply standard MLMs to small ICU datasets within resource-constrained institutions.

ML models were applied to a prospective cohort of critically ill mechanically ventilated patients from a teaching hospital in rural Kenya. Characteristics included an ICU scoring system specifically for resource-constrained environments (Tropical Intensive Care Score (TropICS)). Outputs included AUC of the ROC and the feature importance table. Python-based MLMs included XGBoost and KNN. AUC of the ROC was calculated to predict mortality as the primary endpoint.

There were 294 patients, with an average age of 40.2 years, 64.3% male, 23.8% trauma, and an overall mortality of 60.2%. With respect to mortality patients who died were older (43.5 versus 35 years; p < 0.001), but with no difference in male gender (64.8% versus 63.8%; p = 0.9), or having been transferred from outside facilities (34% versus 21.5%; p = 0.5). Whilst there was no difference in the rate of tachycardia or acidosis, patients who died were more likely to present with hemodynamic instability (31% versus 6%; p < 0.001) and higher clinical severity scores. In predicting mortality, the ML models performed very well (XGBoost AUC = 0.82). Within MLM feature importance, the Tropical Intensive Care Score (TropICS) performed as well as APACHE-II and the SAPS.

ML models can be effectively applied to a small ICU dataset within resource-constrained environments. ML models must demonstrate functionality prior to incorporating within prospective clinical predictive models. Contextualized ICU scoring systems (TropICS) performed well within MLMs.