SYSTEM AND METHOD FOR AUTOMATED FEATURE-BASED ALERT TRIGGERING
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.
1 . A method of triggering an alert with a computing system, the method comprising:
receiving electrical signals corresponding to a plurality of user inputs to a computing system;
automatically generating input-based features from the received electrical signals;
inputting the input-based features into a machine-learning algorithm;
automatically and directly generating a risk prediction with the machine-learning algorithm from the input-based features; and
generating and displaying an alert when the risk prediction exceeds a threshold value.
2 . The method of claim 1 , wherein at least some of the input-based features are meaningful features.
3 . The method of claim 2 , wherein the meaningful features are generated from substance identified in the received electrical signals.
4 . The method of claim 3 , wherein at least some of the input-based features are non-meaningful features.
5 . The method of claim 4 , wherein the non-meaningful features are independent of the substance identified in the received electrical signals.
6 . The method of claim 5 , wherein the features comprise at least two from: a Hurst coefficient; a percent correct on first try; an average score; an average part score; a number of attempted parts; an average number of attempted parts; and an aggregation parameter.
7 . The method of claim 6 , further comprising: generating a response from the received electrical signals; and automatically evaluating the response according to stored evaluation data.
8 . The method of claim 7 , wherein the meaningful features are generated based on the generated response.
9 . The method of claim 7 , wherein at least some of the meaningful features are generated based on the generated response and the evaluation of the response.
10 . The method of claim 1 , wherein the machine-learning algorithm comprises at least one of: a Random Forrest algorithm; an AdaBoost algorithm; a Naïve Bayes algorithm; Boosting Tree, and a Support Vector Machine.
11 . A system for triggering an alert, the system comprising:
memory comprising a model database containing a machine-learning algorithm, wherein the machine-learning algorithm is configured to generate a risk prediction based on inputted features;
a user device configured to receive inputs from a user; and
at least one server configured to:
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.
12 . The system of claim 11 , wherein at least some of the input-based features are meaningful features.
13 . The system of claim 12 , wherein the meaningful features are generated from substance identified in the received electrical signals.
14 . The system of claim 13 , wherein at least some of the input-based features are non-meaningful features.
15 . The system of claim 14 , wherein the non-meaningful features are independent of the substance identified in the received electrical signals.
16 . The system of claim 15 , wherein the features comprise at least two from: a Hurst coefficient; a percent correct on first try; an average score; an average part score; a number of attempted parts; an average number of attempted parts; and an aggregation parameter.
17 . The system of claim 16 , wherein the at least one server is further configured to: generate a response from the received electrical signals; and automatically evaluate the response according to stored evaluation data, wherein the alert comprises a graphical depiction of the risk prediction.
18 . The system of claim 17 , wherein the meaningful features are generated based on the generated response.
19 . The system of claim 17 , wherein at least some of the meaningful features are generated based on the generated response and the evaluation of the response.
20 . The system of claim 11 , wherein the machine-learning algorithm comprises at least one of: a Random Forrest algorithm; an AdaBoost algorithm; a Naïve Bayes algorithm; Boosting Tree, and a Support Vector Machine.