Accurate temporal event predictive modeling

Abstract

Embodiments for accurate temporal event predictive modeling by a processor. An average reverse event delay may be determined from one or more event delays in a time-series window. A time-series event may be predicted by applying the average reverse event delay in conjunction with one or more weighted factors in a predictive model.

1 . A method for accurate temporal event predictive modeling by a processor, comprising:
 determining a reverse event delay for each of a plurality of time-series windows, wherein the reverse event delay is an amount of time between the end of the time-series window and an occurrence of a corresponding hypoglycemic event;   determining an average reverse event delay from the plurality of reverse event delays;   training a predictive model to predict hypoglycemic events using at least the average reverse event delay; and   predicting whether a future hypoglycemic event will occur within a particular time-series window using the predictive model.   
     
     
         2 . The method of  claim 1 , wherein each time-series window has a predetermined length of time, and wherein each time-series window begins at the occurrence of a bolus event. 
     
     
         3 . The method of  claim 1 , wherein each time-series window is a window of time between the occurrence of two bolus events. 
     
     
         4 . The method of  claim 1 , wherein determining the reverse event delay for each of the plurality of time-series windows comprises:
 identifying a first time-series window, wherein the first time-series window is a window that starts at an occurrence of a first bolus event and ends a predetermined amount of time later;   determining whether a first hypoglycemic event occurs within the first time-series window;   if the first hypoglycemic event occurs within the first time-series window, calculating a first reverse event delay of the plurality of reverse event delays as an amount of time between an occurrence of the first hypoglycemic event and the end of the first time-series window; and   if the first time-series event does not occur within the first time-series window, setting the first reverse event delay as zero.   
     
     
         5 . The method of  claim 4 , wherein determining whether the first hypoglycemic events occurs within the first time-series window comprises:
 determining whether a blood glucose level of a patient drops below 70 mg/dl during the first time-series window;   if the blood glucose level of the patient drops below 70 mg/dl during the first time-series window, determining that the first time-series event occurred; and   if the blood glucose level of the patient does not drop below 70 mg/dl during the first time-series window, determining that the first time-series event did not occur.   
     
     
         6 . The method of  claim 1 , wherein the predictive model includes one or more of the group consisting of:
 an artificial neural network, a random forests model, an ensembles of classifiers, a bootstrap aggregating model, and a boosting model.   
     
     
         7 . The method of  claim 1 , the method further comprising:
 determining an event delay for each of the plurality of time-series windows, wherein the event delay is an amount of time between the start of the time-series window and the occurrence of the corresponding hypoglycemic event;   determining an average event delay from the plurality of event delays; and   determining a first weight for the average reverse event delay and a second weight for the average event delay,   wherein the predictive model is a weighted model trained to predict hypoglycemic events using the average reverse event delay in conjunction with the average event delay and the first and second weights.   
     
     
         8 . A system comprising:
 a processor configured to perform a method comprising:   determining a reverse event delay for each of a plurality of time-series windows, wherein the reverse event delay is an amount of time between the end of the time-series window and an occurrence of a corresponding hypoglycemic event;   determining an average reverse event delay from the plurality of reverse event delays;   training a predictive model to predict hypoglycemic events using at least the average reverse event delay; and   predicting whether a future hypoglycemic event will occur within a particular time-series window using the predictive model.   
     
     
         9 . The system of  claim 8 , wherein each time-series window has a predetermined length of time, and wherein each time-series window begins at the occurrence of a bolus event. 
     
     
         10 . The system of  claim 8 , wherein each time-series window is a window of time between the occurrence of two bolus events. 
     
     
         11 . The system of  claim 8 , wherein determining the reverse event delay for each of the plurality of time-series windows comprises:
 identifying a first time-series window, wherein the first time-series window is a window that starts at an occurrence of a first bolus event and ends a predetermined amount of time later;   determining whether a first hypoglycemic event occurs within the first time-series window;   if the first hypoglycemic event occurs within the first time-series window, calculating a first reverse event delay of the plurality of reverse event delays as an amount of time between an occurrence of the first hypoglycemic event and the end of the first time-series window; and   if the first time-series event does not occur within the first time-series window, setting the first reverse event delay as zero.   
     
     
         12 . The system of  claim 11 , wherein determining whether the first hypoglycemic events occurs within the first time-series window comprises:
 determining whether a blood glucose level of a patient drops below 70 mg/dl during the first time-series window;   if the blood glucose level of the patient drops below 70 mg/dl during the first time-series window, determining that the first time-series event occurred; and   if the blood glucose level of the patient does not drop below 70 mg/dl during the first time-series window, determining that the first time-series event did not occur.   
     
     
         13 . The system of  claim 8 , wherein the predictive model includes one or more of the group consisting of:
 an artificial neural network, a random forests model, an ensembles of classifiers, a bootstrap aggregating model, and a boosting model.   
     
     
         14 . The system of  claim 8 , wherein the method further comprises:
 determining an event delay for each of the plurality of time-series windows, wherein the event delay is an amount of time between the start of the time-series window and the occurrence of the corresponding hypoglycemic event;   determining an average event delay from the plurality of event delays; and   determining a first weight for the average reverse event delay and a second weight for the average event delay,   wherein the predictive model is a weighted model trained to predict hypoglycemic events using the average reverse event delay in conjunction with the average event delay and the first and second weights.   
     
     
         15 . A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: determining a reverse event delay for each of a plurality of time-series windows, wherein the reverse event delay is an amount of time between the end of the time-series window and an occurrence of a corresponding hypoglycemic event;
 determining an average reverse event delay from the plurality of reverse event delays;   training a predictive model to predict hypoglycemic events using at least the average reverse event delay; and   predicting whether a future hypoglycemic event will occur within a particular time-series window using the predictive model.   
     
     
         16 . The computer program product of  claim 15 , wherein each time-series window has a predetermined length of time, and wherein each time-series window begins at the occurrence of a bolus event. 
     
     
         17 . The computer program product of  claim 15 , wherein each time-series window is a window of time between the occurrence of two bolus events. 
     
     
         18 . The computer program product of  claim 15 , wherein determining the reverse event delay for each of the plurality of time-series windows comprises:
 identifying a first time-series window, wherein the first time-series window is a window that starts at an occurrence of a first bolus event and ends a predetermined amount of time later;   determining whether a first hypoglycemic event occurs within the first time-series window;   if the first hypoglycemic event occurs within the first time-series window, calculating a first reverse event delay of the plurality of reverse event delays as an amount of time between an occurrence of the first hypoglycemic event and the end of the first time-series window; and   if the first time-series event does not occur within the first time-series window, setting the first reverse event delay as zero.   
     
     
         19 . The computer program product of  claim 18 , wherein determining whether the first hypoglycemic events occurs within the first time-series window comprises:
 determining whether a blood glucose level of a patient drops below 70 mg/dl during the first time-series window;   if the blood glucose level of the patient drops below 70 mg/dl during the first time-series window, determining that the first time-series event occurred; and   if the blood glucose level of the patient does not drop below 70 mg/dl during the first time-series window, determining that the first time-series event did not occur.   
     
     
         20 . The computer program product of  claim 15 , wherein the method further comprises:
 determining an event delay for each of the plurality of time-series windows, wherein the event delay is an amount of time between the start of the time-series window and the occurrence of the corresponding hypoglycemic event;   determining an average event delay from the plurality of event delays; and   determining a first weight for the average reverse event delay and a second weight for the average event delay,   wherein the predictive model is a weighted model trained to predict hypoglycemic events using the average reverse event delay in conjunction with the average event delay and the first and second weights.