Human-in-the-loop interactive model training
Example embodiments relate to a method for training a predictive model from data. The method includes defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features. The method also includes iteratively training a boosting model by generating a number of new random predicates, scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model, selecting a number of the new random predicates with the highest weighted information gain and adding them to the boosting model, computing weights for all the predicates in the boosting model, removing one or more of the selected new predicates with the highest information gain from the boosting model in response to input from an operator. The method may include repeating the prior steps a plurality of times.
1. A computer-implemented method of training a predictive model from data comprising a multitude of features, each feature associated with a real value and a time component, comprising the steps of executing the following instructions in a processor of the computer:
a) defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features;
b) iteratively training a boosting model by performing the following:
1) Generating a number of new random predicates as binary functions operating on at least one of (i) time sequences of the features or (ii) logical operations on the time sequences of the features;
2) Scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model;
3) Selecting, from the new random predicates, a number of the new random predicates that are the highest with respect to their weighted information gain scores and adding them to the boosting model;
4) Computing weights for all the predicates in the boosting model;
5) Removing one or more of the selected number of the new random predicates from the boosting model in response to input from an operator; and
6) Repeating the performance of steps 1, 2, 3, 4 and 5 a plurality of times and thereby generating a final iteratively trained boosting model.
2. The method of claim 1 , further comprising the step of c) evaluating the final iteratively trained boosting model.
3. The method of claim 2 , wherein the evaluation step (c) comprises evaluating the final iteratively trained boosting model for at least one of accuracy, complexity, or trustworthiness.
4. The method of claim 1 , wherein the data is in a tuple format of the type {X, x i , t i } where X is the name of feature, x i is a real value of the feature and t i is a time component for the real value x i , and wherein the predicates are defined as binary functions operating on at least one of (i) sequences of tuples or (ii) logical operations on sequences of the tuples.
5. The method of claim 4 , wherein the sequences of tuples are defined by time periods selected from the group consisting of 1 or more days, 1 or more hours, 1 or more minutes, or 1 or more months.
6. The method of claim 1 , wherein the data comprises electronic health record data for a multitude of patients.
7. The method of claim 1 , wherein the method further comprises the step of dividing the predicates into groups based on understandability, namely a first group of relatively more human understandable predicates and a second group of relatively less human understandable predicates and wherein the new random predicates are selected from the first group.
8. The method of claim 7 , wherein the data comprises electronic health record data for a multitude of patients, and wherein the set of predicates are represented in a manner to show the subject matter or source within the electronic health record data of the predicates.
9. The method of claim 8 , wherein the predicates comprise an existence predicate returning a result of 0 or 1 depending on whether a feature exists in the electronic health record data for a given patient in the multitude of patients; and a counts predicate returning a result of 0 or 1 depending on the number of counts of a feature in the electronic health record data for a given patient in the multitude of patients relative to a numeric parameter C.
10. The method of claim 1 , wherein step b) 5) further comprises the step of graphically representing the predicates currently in the boosting model and providing the operator with the ability to remove one or more of the predicates.
11. The method of claim 10 , further comprising the step of graphically representing the weights computed for each of the predicates in step b) 4).
12. The method of claim 1 , further comprising the step of graphically representing a set of predicates added to the boosting model after each of the iterations of step b) 6).
13. The method of claim 1 , wherein step b) further comprises the step of providing the operator with the ability to define a predicate during model training.
14. The method of claim 1 , wherein step b) further comprises the step of removing redundant predicates.
15. The method of claim 1 , further comprising the step of ranking the predicates selected in step b) 3).
16. The method of claim 1 , further comprising the step of generating statistics of predicates in the boosting model and presenting them to the operator.
17. The method of claim 1 , wherein in step b) 5) the one or more predicates are removed which are not causally related to the prediction of the boosting model.
18. A computer-implemented method of training a predictive model from electronic health record data for a multitude of patients, the data comprising a multitude of features, each feature associated with real values and a time component, wherein the data is in a tuple format of the type {X, x i , t i } where X is the name of feature, x i is a real value of the feature and t i is a time component for the real value x i , comprising the steps of implementing the following instructions in a processor of the computer:
a) defining a multitude of predicates as at least one of (i) binary functions operating on sequences of the tuples or (ii) logical operations on the sequences of the tuples;
b) dividing the multitude of predicates into groups based on understandability, namely a first group of relatively more human understandable predicates and a second group of relatively less human understandable predicates;
c) iteratively training a boosting model by performing the following:
1) Generating a number of new random predicates from the first group of predicates as binary functions operating on at least one of (i) sequences of the tuples or (ii) logical operations on the sequences of the tuples;
2) Scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model;
3) Selecting, from the new random predicates, a number of the new random predicates that are the highest with respect to their weighted information gain scores and adding them to the boosting model;
4) Computing weights for all the predicates in the boosting model;
5) Removing one or more of the selected number of the new random predicates from the boosting model in response to input from an operator; and
6) Repeating the performance of steps 1, 2, 3, 4 and 5 a plurality of times and thereby generating a final iteratively trained boosting model.
19. The method of claim 18 , further comprising the step d) of evaluating the final iteratively trained boosting model.
20. A workstation for providing operator input into iteratively training a boosting model, wherein the workstation comprises an interface and a processor, and wherein the processor is configured to perform operations comprising:
1) Generating a number of new random predicates as binary functions operating on at least one of (i) time sequences of input features or (ii) logical operations on the time sequences of the input features;
2) Scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model;
3) Selecting, from the new random predicates, a number of the new random predicates that are the highest with respect to their weighted information gain scores and adding them to the boosting model;
4) Computing weights for all the predicates in the boosting model;
5) Removing one or more of the selected number of the new random predicates from the boosting model in response to input from an operator, wherein the input is received by way of the interface; and
6) Repeating the performance of steps 1, 2, 3, 4 and 5 a plurality of times and thereby generating a final iteratively trained boosting model.
21. The workstation of claim 20 , wherein predicates are defined as at least one of (i) binary functions operating on sequences of features having both a real value component and a time component or (ii) logical operations on sequences of the features.
22. The workstation of claim 20 , wherein the operations further comprise the step of graphically representing on the interface a set of predicates added to the boosting model after each of the iterations of step 6).
23. The workstation of claim 20 , wherein the step (i) of automatically generating the plurality of additional predicates comprises:
(a) generating candidate predicates by a pseudo-random algorithm;
(b) scoring the candidate predicates for weighted information gain in the boosting model; and
(c) selecting the additional predicates from the candidate predicates based on the scores.
24. The workstation of claim 23 , wherein the output of each predicate is a binary value.
25. The workstation of claim 23 , wherein each sample in the training data is formatted as a plurality of data items having a tuple format of the type {X, x i , t i }, wherein x i indicates the value of feature X at a time t i , and i labels the tuple of the sample, and wherein each predicate is defined as a function performed on a plurality of data items of the sample.
26. The workstation according to claim 23 , wherein the training data comprises electronic health record data for a plurality of patients.
27. The workstation of claim 23 , wherein each predicate is a function of a part of the sample relating to a single corresponding one of the features.
28. The workstation according to claim 23 , wherein the features are each associated with a corresponding one of a set of human understandable categories or groups, and step (iii) of displaying a plurality of the set of predicates includes displaying grouped together the predicates which are functions of data relating to features of each category or group.
29. The workstation of claim 23 , wherein step (iii) of displaying a plurality of the set of predicates includes displaying a respective weight value of the boosting model.
30. The workstation of claim 23 , further comprising evaluating the accuracy of the boosting model in predicting the label using a validation sub-set of the training data.
31. The workstation of claim 23 , wherein the rejected one or more of the updated set of predicates are not causally related to a prediction of the boosting model.
32. A computer-implemented method of generating a predictive model from training data, the predictive model being for predicting a label based on input data which, for each of a plurality of features X, indicates a value x of the feature at each of a plurality of times, and the training data comprising a plurality of samples, each sample indicating the value of one or more of the features at each of one of more times and a corresponding label; the method comprising implementing the following steps as instructions with a processor:
defining a set of predicates, each predicate being a function which generates an output when applied to time sequences of the features or logical combinations of the time sequences of the features;
generating a boosting model, the boosting model receiving as input the respective outputs of each of the set of predicates when applied to the samples of the training data; and
performing a plurality of times, the sequence of steps of:
(i) automatically generating a plurality of additional predicates as binary functions operating on at least one of (i) time sequences of the features or (ii) logical operations on the time sequences of the features;
(ii) adding the plurality of additional predicates to predicates already in the boosting model to form an updated set of predicates;
(iii) displaying a plurality of the updated set of predicates; and
(iv) receiving data input rejecting one or more of the updated set of predicates; and
(v) removing the rejected one or more predicates from the updated set of predicates.
33. The method of claim 32 , wherein the additional predicates comprise at least one of existence predicates which are each indicative of a specific feature taking a value in a specific range at least one time, and count predicates which are each indicative of a specific feature taking a value in a specific range at more than, less than, or equal to a specific number of times C.