Macrophage activation syndromes severity prediction

A method for automatically modifying a database record associated with a patient based on a probability of acute deterioration for the patient, the method comprising:

• communicatively coupling a patient monitoring device with an electronic health record (EHR);
• detecting addition of a plurality of serial laboratory measurements or a plurality of serial physiological measurements corresponding to a plurality of measurement variables for the patient to a database maintaining the EHR of the patient, the addition made to the database by the patient monitoring device having a sensor that acquires data from the patient, wherein the EHR includes unstructured data;
• responsive to detecting the addition and an indication in the EHR of the patient that the patient is in a facility, calling a first programmatic function that extracts the plurality of serial laboratory measurements and the plurality of serial physiological measurements for the patient from the EHR of the patient;
• transforming the plurality of serial laboratory measurements and the plurality of serial physiological measurements to create conditioned patient information by calling a second programmatic function that chronologically orders the plurality of serial laboratory measurements and the plurality of serial physiological measurements using a date-time stamp to construct time series of corresponding addition of each serial laboratory measurement and serial physiological measurement of the plurality of serial laboratory measurements and the plurality of serial physiological measurements;
• determining a statistical dependency of a first measurement variable of the plurality of measurement variables on one or more measurement variables of the plurality of measurement variables using a copula regression, wherein the first measurement variable include the time series of transgressions of reference threshold values for laboratory or physical measurements and the one or more measurements variables include the time series of a shock index derived from synchronously measured heart rate and systolic blood pressure;
• computing a value representative of a quantity of threshold transgressions in the copula regression of the conditioned patient information;
• calculating a predicted probability of acute deterioration by inputting the statistical dependency and the value to a model trained to generate the predicted probability of the acute deterioration based on the input, wherein the predicted probability of acute deterioration represents a forecast probability of current and future acute;
• comparing the predicted probability of acute deterioration to a threshold probability;
• responsive to the predicted probability of acute deterioration exceeding the threshold probability, initiating undertaking an immunomodulatory treatment.