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Automated Classification and Entity Extraction from essential documents pertaining to Clinical Trials

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 the 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 air-gapped network.

Understand Patient Experience Journey to Improve Pharma Value Chain

Patient experience information available in public data sources such as social media and case reports is of immense value to Pharma industries....