Abstract: In some examples, a patient care pathway is coupled with a cytokine release syndrome (CRS) prediction system. A CRS prediction machine learning model is used to analyze patient-related health data associated with a monitored user, such as a patient. The health data includes physiological data obtained from sensor devices associated with the monitored patient and user-provided data associated with the monitored patient. A CRS event prediction indicates the probability of an occurrence of a CRS event within a time-period after the prediction is generated. A CRS event that is predicted or detected in progress is graded to indicate a predicted severity. An outcome can also be generated indicating whether the patient's condition is predicted to improve within the future time-period, enabling more accurate early detection of CRS events for improved patient outcomes. In some examples, the prediction can facilitate a patient care pathway for improved, safer, and more cost-effective care.