**5.3 Model surveillance**

Machine learning (ML) models for sepsis are notorious for creating alerts that are not actionable. In addition, these models' predictive performance degrades over time especially when deployed on populations not resembling their training sets. Concept drift, or the change in the underlying data distribution over time, is often not considered in the deployment of ML models. Many companies that provide sepsis ML detection systems fail to account for new data or changes in patient demographics.

For example, let us examine the following example (**Figure 4**) [28]. Models built in states with low death rates will perform poorly when being deployed in states with high death rates and vice versa due to overfitting to a particular population/dataset. Both data drift and concept drift can occur at the same time, leading to inaccurate predictions and reduced model efficacy. It is crucial to incorporate methods that can handle data drift, concept drift and population drift in the maintenance and deployment of ML models, especially in the clinical setting where predictions have an impact on patient outcomes. One solution to these issues is continuously incorporating prospective data to re-calibrate the model. In the case of the CS-DI, if the

*Developing and Deploying a Sepsis Deterioration Machine Learning Algorithm DOI: http://dx.doi.org/10.5772/intechopen.111557*

#### **Figure 4.** *Septicemia mortality by state.*

model predicted sepsis when a patient was not septic, the model should eventually be retrained to correctly categorize that patient.
