SYNTHETICALLY GENERATED HEALTHCARE DOCUMENTS FOR CLASSIFIER TRAINING

A synthetic generation of healthcare documents for use in training a classifier is described herein. Initially, a multiplicity of electronic forms are received in memory of a host computing system and data extracted from a specific common field located in each of the forms. A statistical metric is t...

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Bibliographic Details
Main Authors Osten, Tim, Sublett, Andre, Scott, John
Format Patent
LanguageEnglish
Published 28.12.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
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Summary:A synthetic generation of healthcare documents for use in training a classifier is described herein. Initially, a multiplicity of electronic forms are received in memory of a host computing system and data extracted from a specific common field located in each of the forms. A statistical metric is then computed for the specific common field a value synthetically generated for the specific common field according to the computed statistical metric. Finally, the synthetically generated value is inserted into the specific common field of a training version of the electronic forms and the training version of the electronic forms persisted as part of a training data set for a classifier adapted to classify the multiplicity of electronic forms.
Bibliography:Application Number: US202217846113