System and method for automated preemptive alert triggering

Abstract

Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.

What is claimed is: 
     
         1 . A system for triggering a pre-emptive alert, the system comprising:
 memory comprising a machine-learning classifier configured to generate a risk prediction based on inputted features;   a first user device configured to receive inputs from a user;   a second user device configured to display information to a user; and   at least one server configured to:
 receive electrical signals corresponding to user inputs to the first user device; 
 generate a set of input-based features from the received electrical signals; 
 select a sub-set of the input-based features from the set of features; 
 input the sub-set of the features into the machine-learning classifier; 
 generate a risk prediction with the machine-learning classifier; and 
 control the second user device to display an alert when the risk prediction exceeds a threshold value. 
   
     
     
         2 . The system of  claim 1 , wherein the sub-set of features comprises at least one meaningful feature. 
     
     
         3 . The system of  claim 2 , wherein the at least one meaningful feature is generated from substance identified in the received electrical signals. 
     
     
         4 . The system of  claim 3 , wherein the sub-set of features comprises at least one non-meaningful features. 
     
     
         5 . The system of  claim 4 , wherein the at least one non-meaningful feature is independent of the substance identified in the received electrical signals. 
     
     
         6 . The system of  claim 5 , wherein the classifier comprises a linear classifier. 
     
     
         7 . The system of  claim 5 , wherein the classifier comprises a probabilistic classifier. 
     
     
         8 . The system of  claim 5 , wherein the classifier comprises a Random forest classifier. 
     
     
         9 . The system of  claim 1 , wherein inputting the sub-set of the features into the machine learning classifier comprises: generating a feature vector for each of the features in the sub-set of features; and inputting the feature vectors into the classifier. 
     
     
         10 . The system of  claim 9 , wherein the alert comprises a graphical depiction of the risk prediction. 
     
     
         11 . A method of triggering a pre-emptive alert with a computing system, the method comprising:
 receiving electrical signals corresponding to a plurality of user inputs to a computing system;   automatically generating a set of input-based features from the received electrical signals;   selecting a sub-set of the input-based features from the set of input-based features;   inputting the sub-set of the input-based features into a machine-learning algorithm;   generating a risk prediction with the machine-learning algorithm from the input-based features; and   displaying an alert when the risk prediction exceeds a threshold value.   
     
     
         12 . The method of  claim 11 , wherein the sub-set of features comprises at least one meaningful feature. 
     
     
         13 . The method of  claim 12 , wherein the at least one meaningful feature is generated from substance identified in the received electrical signals. 
     
     
         14 . The method of  claim 13 , wherein the sub-set of features comprises at least one non-meaningful feature. 
     
     
         15 . The method of  claim 14 , wherein the at least one non-meaningful feature is independent of the substance identified in the received electrical signals. 
     
     
         16 . The method of  claim 15 , wherein the machine-learning algorithm comprises a classifier, wherein the classifier comprises a linear classifier. 
     
     
         17 . The method of  claim 15 , wherein the machine-learning algorithm comprises a classifier, wherein the classifier comprises a probabilistic classifier. 
     
     
         18 . The method of  claim 15 , wherein the machine-learning algorithm comprises a classifier, wherein the classifier comprises a Random forest classifier. 
     
     
         19 . The method of  claim 11 , wherein inputting the sub-set of the features into the machine-learning algorithm comprises: generating a feature vector for each of the features in the sub-set of features; and inputting the feature vectors into the machine-learning algorithm. 
     
     
         20 . The method of  claim 19 , wherein the alert comprises a graphical depiction of the risk prediction.