METHODS AND SYSTEMS FOR PREDICTING CLINICAL TRIAL CRITERIA USING MACHINE LEARNING TECHNIQUES

1 . A method comprising:

training a machine learning (ML) model to identify an intended respondent for a criterion;

receiving a plurality of criteria associated with a first clinical trial;

determining a respective intended respondent for each of the plurality of criteria based on analyzing the plurality of criteria using the ML model; and

associating each of the plurality of criteria with an indication of the corresponding intended respondent.

2 . The method of claim 1 , wherein training the ML model comprises providing labeled training data to the ML model during a training phase.

3 . The method of claim 1 , wherein the intended respondent for each of the plurality of criteria indicates a qualification required to qualify a user to respond to the respective criterion.

4 . The method of claim 3 , the method further comprising:

receiving, from a first respondent, a first response to a first criterion of the plurality of criterion;

determining a qualification of the first respondent; and

upon determining that the qualification of the first respondent is below a qualification of the intended respondent for the first criterion, rejecting the first response.

5 . The method of claim 1 , the method further comprising:

training a second machine learning (ML) model to identify a time at which a criterion should be answered;

determining a respective time at which each of the plurality of criteria should be answered, based on analyzing the plurality of criteria using the second ML model; and

associating each of the plurality of criteria with an indication of the corresponding time.

6 . The method of claim 5 , the method further comprising:

receiving, at a first time, a first response to a first criterion of the plurality of criterion; and

upon determining that the first time is prior to the time at which the first criterion should be answered, rejecting the first response.

7 . The method of claim 1 , the method further comprising:

training a third machine learning (ML) model to identify a predefined event that must occur prior to answering a criterion;

determining a predefined event that must occur for each of the plurality of criteria, based on analyzing the plurality of criteria using the third ML model; and

associating each of the plurality of criteria with an indication of the corresponding predefined event.

8 . The method of claim 5 , the method further comprising:

receiving, at a first time, a first response to a first criterion of the plurality of criterion; and

upon determining that the predefined event corresponding to the first criterion has not occurred, rejecting the first response.

9 . A system, comprising:

a processor; and

a memory having instructions stored thereon which, when executed by the processor, performs an operation, the operation comprising:

training a machine learning (ML) model to identify an intended respondent for a criterion;

receiving a plurality of criteria associated with a first clinical trial;

determining a respective intended respondent for each of the plurality of criteria based on analyzing the plurality of criteria using the ML model; and

associating each of the plurality of criteria with an indication of the corresponding intended respondent.

10 . The system of claim 9 , wherein training the ML model comprises providing labeled training data to the ML model during a training phase.

11 . The system of claim 9 , wherein the intended respondent for each of the plurality of criteria indicates a qualification required to qualify a user to respond to the respective criterion.

12 . The system of claim 11 , wherein the operation further comprises:

receiving, from a first respondent, a first response to a first criterion of the plurality of criterion;

determining a qualification of the first respondent; and

upon determining that the qualification of the first respondent is below a qualification of the intended respondent for the first criterion, rejecting the first response.

13 . The system of claim 9 , wherein the operation further comprises:

training a second machine learning (ML) model to identify a time at which a criterion should be answered;

determining a respective time at which each of the plurality of criteria should be answered, based on analyzing the plurality of criteria using the second ML model; and

associating each of the plurality of criteria with an indication of the corresponding time.

14 . The system of claim 13 , wherein the operation further comprises:

receiving, at a first time, a first response to a first criterion of the plurality of criterion; and

upon determining that the first time is prior to the time at which the first criterion should be answered, rejecting the first response.

15 . The system of claim 9 , wherein the operation further comprises:

training a third machine learning (ML) model to identify a predefined event that must occur prior to answering a criterion;

determining a predefined event that must occur for each of the plurality of criteria, based on analyzing the plurality of criteria using the third ML model; and

associating each of the plurality of criteria with an indication of the corresponding predefined event.

16 . The system of claim 15 , wherein the operation further comprises:

receiving, at a first time, a first response to a first criterion of the plurality of criterion; and

upon determining that the predefined event corresponding to the first criterion has not occurred, rejecting the first response.

17 . A computer-readable medium having instructions stored thereon which, when executed by a processor, performs an operation, the operation comprising:

training a machine learning (ML) model to identify an intended respondent for a criterion;

receiving a plurality of criteria associated with a first clinical trial;

determining a respective intended respondent for each of the plurality of criteria based on analyzing the plurality of criteria using the ML model; and

associating each of the plurality of criteria with an indication of the corresponding intended respondent.

18 . The computer-readable medium of claim 17 , wherein the intended respondent for each of the plurality of criteria indicates a qualification required to qualify a user to respond to the respective criterion, and wherein the operation further comprises:

receiving, from a first respondent, a first response to a first criterion of the plurality of criterion;

determining a qualification of the first respondent; and

upon determining that the qualification of the first respondent is below a qualification of the intended respondent for the first criterion, rejecting the first response.

19 . The computer-readable medium of claim 17 , wherein the operation further comprises:

training a second machine learning (ML) model to identify a time at which a criterion should be answered;

determining a respective time at which each of the plurality of criteria should be answered, based on analyzing the plurality of criteria using the second ML model; and

associating each of the plurality of criteria with an indication of the corresponding time.

20 . The computer-readable medium of claim 17 , wherein the operation further comprises:

training a third machine learning (ML) model to identify a predefined event that must occur prior to answering a criterion;

determining a predefined event that must occur for each of the plurality of criteria, based on analyzing the plurality of criteria using the third ML model; and

associating each of the plurality of criteria with an indication of the corresponding predefined event.