Unitedhealth Group Incorporated

Joel David Stremmel

Eran Halperin

Ardavan Saeedi

Sanjit S Batra

Hamid Reza Hassanzadeh

Brian Hill

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving question-answer (QA) machine learning model training based on generating predicted label indicators, generating prediction score indicators and prediction explanation indicators, generating structured label-explanation datasets, generating synthetic QA training datasets, generating a prediction output, and initiating the performance of one or more prediction-based operations based on the prediction output.

Generation of synthetic question-answer pairs using a document classifier and classification explainer

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.

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Iowa City, IA, United States of America

Joel David Stremmel