A method of determining patient triage categories, the method comprising: on a triage system , developing a machine learning model that includes a classifier , the triage system having a processor and a non-transitory computer-readable medium storing instructions, the instructions when translated by the processor implementing a triage module having an interface and the machine learning model , the developing comprising: inputting historical patient data to the triage module through the interface , the historical patient data including patient data of a plurality of patients, the patient data including attributes describing the plurality of patients in a pre-hospital setting, the attributes including triage categories assigned to the plurality of patients in the pre-hospital setting; and segmenting the historical patient data into a training set and a testing set; constructing the classifier using the training set and the testing set, the classifier configured to take as input at least one value for the attributes and, based on the at least one value, determine a patient triage category; and tuning the classifier according to a desired over triage rate and a desired under triage rate, wherein the tuning the classifier comprises setting the desired over triage rate or the desired under triage rate according to at least one criteria including a geography where the triage system is to be deployed; receiving, through the interface from devices in the pre-hospital setting patient attribute data describing a patient in the pre-hospital setting; applying the machine learning model to the collected patient attribute data to generate a triage category for the patient in the pre-hospital setting; and presenting, through the interface , the triage category for the patient in the pre-hospital setting. inputting historical patient data to the triage module through the interface , the historical patient data including patient data of a plurality of patients, the patient data including attributes describing the plurality of patients in a pre-hospital setting, the attributes including triage categories assigned to the plurality of patients in the pre-hospital setting; and segmenting the historical patient data into a training set and a testing set; constructing the classifier using the training set and the testing set, the classifier configured to take as input at least one value for the attributes and, based on the at least one value, determine a patient triage category; and tuning the classifier according to a desired over triage rate and a desired under triage rate, wherein the tuning the classifier comprises setting the desired over triage rate or the desired under triage rate according to at least one criteria including a geography where the triage system is to be deployed; receiving, through the interface from devices in the pre-hospital setting patient attribute data describing a patient in the pre-hospital setting; applying the machine learning model to the collected patient attribute data to generate a triage category for the patient in the pre-hospital setting; and presenting, through the interface , the triage category for the patient in the pre-hospital setting. <EOS>