Abstract: Provided are a system and a method for prediction of an intradialytic adverse event, where a machine learning model of two-class classification is utilized to predict intradialytic adverse events in quasi-real time, such that features extracted in an ongoing hemodialysis process in real time can have the hemodialysis session alerted for forthcoming adverse events. Therefore, clinicians can be warned to take necessary actions and adjust the hemodialysis machine settings in advance. In addition, a computer readable medium thereof is also provided.

Abstract: Provided are a system and a method for prediction of an intradialytic adverse event, where a machine learning model of two-class classification is utilized to predict intradialytic adverse events in quasi-real time, such that features extracted in an ongoing hemodialysis process in real time can have the hemodialysis session alerted for forthcoming adverse events. Therefore, clinicians can be warned to take necessary actions and adjust the hemodialysis machine settings in advance. In addition, a computer readable medium thereof is also provided.