Machine learning enabled prognosis of patient mortality

A system, comprising:

• at least one data processor;
• and
• at least one memory storing instructions, which when executed by the at least one data processor, result in operations comprising:
• training, based at least on a set of labeled training samples, a mortality prognosis model to determine a risk of patient mortality within a given timeframe;
• applying the trained mortality prognosis model to determine, based at least on a health record of a patient, a risk of mortality for the patient within the given timeframe;
• and
• determining, based at least on the risk of mortality for the patient within the given timeframe, a treatment plan for the patient.

A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

• training, based at least on a set of labeled training samples, a mortality prognosis model to determine a risk of patient mortality within a given timeframe;
• applying the trained mortality prognosis model to determine, based at least on a health record of a patient, a risk of mortality for the patient within the given timeframe;
• and
• determining, based at least on the risk of mortality for the patient within the given timeframe, a treatment plan for the patient.