System and method of modeling irregularly sampled temporal data using kalman filters

A method for identifying a patient at risk of sepsis said method comprising:

• collecting a dataset of at least one vital sign of said patient;
• defining a state equation according to said collected dataset;
• defining a temporal difference variable;
• incorporating said temporal difference variable into said state equation in order to form a linear dynamic system model;
• and
• forecasting the onset of sepsis in said patient according to said linear dynamic system model.

A clinical decision support system comprising:

• at least one dataset collection module configured to collect data;
• a processor-readable medium storing computer code representing instructions to cause a process for identifying content relevance, said computer code comprising code to:
• define a state equation according to said collected dataset with said irregular temporal sampling rate;
• define a temporal difference variable to account for said irregular temporal sampling rate;
• incorporate said temporal difference variable into said state equation in order to form a linear dynamic system model;
• and
• forecast future expected data in said dataset according to said linear dynamic system model.