Forecasting arterial embolic and bleeding events

A computer-implemented method for preventing or managing clinical deterioration associated with at least one patient, the computer-implemented method performed by one or more hardware processors (OMHPs) and comprising:

• generating a patient forecast via the OMHPs, wherein generating the patient forecast via the OMHPs comprises:
• collecting physiologic data from a set of sensors that are in proximity to the at least one patient and that are configured to generate time-stamped physiological measurements relating to the at least one patient;
• receiving the physiologic data as a set of patient data based on collecting the physiologic data, the received set of patient data comprising the time-stamped physiological measurements;
• generating a time series, that corresponds to the received set of patient data, in a form of a Hankel matrix;
• and
• producing a patient forecast using a set of evolutionary algorithms comprising at least one of particle swarm optimization or differential evolution, wherein:
• noise is removed from the time series by utilizing the set of evolutionary algorithms to evaluate an objective function of the Hankel matrix comprising noise-compensating error terms, and
• the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on the time series;
• comparing the patient forecast with control limit information associated with the physiologic data;
• determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information;
• and
• based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating a set of instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.

A patient monitoring system having one or more hardware processors (OMHPs) configured to perform a plurality of operations for preventing or managing clinical deterioration associated with at least one patient, the operations comprising:

• generating a patient forecast via the OMHPs, wherein generating the patient forecast via the OMHPs comprises:
• collecting physiologic data from a set of sensors that are in proximity to the at least one patient and that are configured to generate time-stamped physiological measurements relating to the at least one patient;
• receiving the physiologic data as a set of patient data based on collecting the physiologic data, the received set of patient data comprising the time-stamped physiological measurements;
• generating a time series, that corresponds to the received set of patient data, in a form of a Hankel matrix;
• and
• producing a patient forecast using a set of evolutionary algorithms comprising at least one of particle swarm optimization or differential evolution, wherein:
• noise is removed from the time series by utilizing the set of evolutionary algorithms to evaluate an objective function of the Hankel matrix comprising noise- compensating error terms, and
• the patient forecast comprises a predicted physiologic parameter for the at least one patient at a future time based on the time series;
• comparing the patient forecast with control limit information associated with the physiologic data;
• determining, in response to the comparing, whether the patient forecast meets a condition associated with the control limit information;
• and
• based on the predicted physiologic parameter and based further on the patient forecast meeting the condition, generating a set of instructions to present via an electronic user interface indicating notification content selected from a group comprising modifying a treatment program associated with the at least one patient and preventing a future occurrence corresponding to the patient forecast.