  Machine learning is used in medicine to support physicians in examination,
diagnosis, and predicting outcomes. One of the most dynamic area is the usage
of patient generated health data from intensive care units. The goal of this
paper is to demonstrate how we advance cross-patient ML model development by
combining the patient's demographics data with their physiological data. We
used a population of patients undergoing Carotid Enderarterectomy (CEA), where
we studied differences in model performance and explainability when trained for
all patients and one patient at a time. The results show that patients'
demographics has a large impact on the performance and explainability and thus
trustworthiness. We conclude that we can increase trust in ML models in a
cross-patient context, by careful selection of models and patients based on
their demographics and the surgical procedure.
