False positive analysis of machine-learning based sepsis prediction

Background: Despite the emergence of several promising machine learning models for prediction of patients at risk of sepsis, investigation of factors that contribute to false positive rates has not been performed. Here, we conducted a false positive analysis to determine the sources of false alerts and examine a mitigation methodology for reduction of false positive rates in sepsis prediction. Methods Analysis of false positive predictions from our sepsis prediction model was conducted and patient populations were stratified by underlying conditions that contribute to false positives. Sensitivity and specificity results for each subpopulation were calculated while adjusting the initial adjustment threshold. An optimal threshold was applied to an entire test set to observe the effect on performance. Results Certain underlying conditions mimic sepsis and contribute to false positive rates. The optimal threshold adjustment for the whole population resulted in greater reductions of false positives than true positives. Conclusions An optimal threshold adjustment to a whole population based on specific patient populations stratified by underlying conditions reduces the number of false positive cases while increasing the specificity and positive predictive value and still maintaining high sensitivity.