Developing adaptable predictive analytics for subjects in medical facilities

A system for developing adaptable predictive analytics for subjects in a medical facility, the system comprising:

• an interface for receiving initial annotations indicating times for diagnosis of an adverse medical event in subjects of the medical facility;
• a processor in communication with the interface;
• and
• a memory that stores instructions that, when executed by the processor, causes the processor to perform a method comprising:
• training a predictive algorithm for predicting the adverse medical event at a desired notification time before occurrence of the adverse medical event using the initial annotations;
• determining real risk scores over time using the predictive algorithm applied to the subjects in the medical facility, and identifying a corresponding trend of the real risk scores, wherein the real risk scores indicate actual probabilities of the adverse medical event occurring at predetermined times before the occurrence of the adverse medical event;
• creating required risk scores over time based on the real risk score trend, wherein the required risk scores indicate modified probabilities of the adverse medical event occurring at the predetermined times before the occurrence of the adverse medical event;
• mapping the initial annotations to a time-series of new annotations that minimizes differences between the required risk scores and the real risk scores;
• fine-tuning the predictive algorithm for predicting the adverse medical event at the desired notification time using the time-series of new annotations;
• and
• monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time.

A non-transitory computer readable medium that stores instructions that, when executed by a computer processor, performs a method for developing adaptable predictive analytics for subjects in a medical facility by executing steps comprising:

• receiving initial annotations indicating times for diagnosis of an adverse medical event in subjects of the medical facility;
• training a predictive algorithm for predicting the adverse medical event at a desired notification time before occurrence of the adverse medical event using the initial annotations;
• determining real risk scores over time using the predictive algorithm applied to the subjects in the medical facility, and identifying a corresponding trend of the real risk scores, wherein the real risk scores indicate actual probabilities of the adverse medical event occurring at predetermined times before the occurrence of the adverse medical event;
• creating required risk scores over time based on the real risk score trend, wherein the required risk scores indicate modified probabilities of the adverse medical event occurring at the predetermined times before the occurrence of the adverse medical event;
• mapping the initial annotations to a time-series of new annotations that minimizes differences between the required risk scores and the real risk scores;
• and
• fine-tuning the predictive algorithm for predicting the adverse medical event at the desired notification time using the time-series of new annotations, and for monitoring at least one of the subjects in the medical facility by applying the fine-tuned predictive algorithm to indicate the desired notification time.