Machine-learning techniques for generating data-collection scripts

A system comprising:

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to perform operations comprising:
• accessing input data, wherein the input data includes a clinical-trial protocol and contextual data, wherein the clinical-trial protocol is associated with a particular clinical trial, and wherein the contextual data identifies one or more additional characteristics associated with the particular clinical trial;
• processing the input data using a machine-learning model to generate a data-collection script, wherein the machine-learning model corresponds to a transformer model trained using a training dataset that includes previous clinical-trial protocols across one or more clinical domains, and wherein the data-collection script includes a set of questions for screening a plurality of candidates associated with the particular clinical trial;
• generating evaluation metrics associated with the data-collection script, wherein an evaluation metric is associated with a particular question of the set of questions, and wherein the evaluation metric estimates a degree of effectiveness of the particular question in identifying one or more ineligible candidates from the plurality of candidates;
• generating a modified data-collection script based on the evaluation metrics, wherein the modified data-collection script includes a subset of the set of questions, and wherein a question of the subset includes an evaluation metric that exceeds an evaluation-threshold value;
• and
• transmitting the modified data-collection script to candidates associated with the particular clinical trial.

A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to perform operations comprising:

• accessing input data, wherein the input data includes a clinical-trial protocol and contextual data, wherein the clinical-trial protocol is associated with a particular clinical trial, and wherein the contextual data identifies one or more additional characteristics associated with the particular clinical trial;
• processing the input data using a machine-learning model to generate a data-collection script, wherein the machine-learning model corresponds to a transformer model trained using a training dataset that includes previous clinical-trial protocols across one or more clinical domains, and wherein the data-collection script includes a set of questions for screening a plurality of candidates associated with the particular clinical trial;
• generating evaluation metrics associated with the data-collection script, wherein an evaluation metric is associated with a particular question of the set of questions, and wherein the evaluation metric estimates a degree of effectiveness of the particular question in identifying one or more ineligible candidates from the plurality of candidates;
• generating a modified data-collection script based on the evaluation metrics, wherein the modified data-collection script includes a subset of the set of questions, and wherein a question of the subset includes an evaluation metric that exceeds an evaluation-threshold value;
• and
• transmitting the modified data-collection script to candidates associated with the particular clinical trial.