Automated Classification & Entity Extraction from essential documents pertaining to Clinical Trials
Presented by: Nirjhar Sarkar – Technical Design Expert at Novartis and Veysel Kocaman – Principal Data Scientist and ML Engineer at John Snow Labs
An AI based solution that delivers a future-proof model using transfer learning which can be used to convert source-agnostic unstructured data into structured data. It supports classification of artifacts and sub-artifacts and extraction of metadata that are defined in TMF Reference Model. The core pipeline comprises OCR based text extraction, language detection, layout & content based document classifiers, more than 40 different DL based named entity recognition models, each of which is trained on a set of document types and extracting various target entities given the document type, handwritten text detection, handwritten date extraction and artifact-based post processing rules to automate the migration between different document management systems in an airgapped network.
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