biomedical language processing. powered by machine learning. using big data.

Built and trained for pharma business use cases, tellic’s cutting edge automated machine learning text curation engine lets you process billions of text documents with PhD level accuracy

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biomedical nlp

Purpose-built for biomedical text, machine learning pipelines process unstructured data

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Concept search

+6X increase in results & ranking by what is most relevant to quickly deliver insights your scientists

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Knowledge graph

Automated pipeline generates a knowledge graph uncovering insights  that enable data-driven discovery decisions

What makes us different is that we are designed for pharma and customize for your organizational language.

  • Our solution delivers PhD accuracy for detecting biomedical concepts in text data

  • Proprietary machine learning links biomedical concepts into preferred ontologies

  • Context-driven ranking delivers the most important research based on concept

  • Support for company specific jargon, ontologies and identifiers that evolve over time

  • Integrated solution of proprietary and best-in-class packages is engineered to scale for big data

  • Robust preprocessing and domain specific metadata enhances downstream processes

THE POWER OF OUR PIPELINE

Employing a combination of dictionary terms and hand coded rules, most search solutions for biomedical data lack the ability handle different structures of text data and therefore fail to deliver useful results, and lack utility for scientists to focus in on what’s most important to them. tellic’s machine learning and AI technology extracts common terms from biomedical text then to help scientists.  1. Identify broader sets of relevant documents by recognizing synonyms and other terms related to a biomedical concept  2. Pinpoint and remove false positives results from the set of relevant documents  3. Surface most relevant documents with metadata to easily drill down to a smaller, highly relevant set of results for a biomedical concept

Employing a combination of dictionary terms and hand coded rules, most search solutions for biomedical data lack the ability handle different structures of text data and therefore fail to deliver useful results, and lack utility for scientists to focus in on what’s most important to them. tellic’s machine learning and AI technology extracts common terms from biomedical text then to help scientists.

1. Identify broader sets of relevant documents by recognizing synonyms and other terms related to a biomedical concept

2. Pinpoint and remove false positives results from the set of relevant documents

3. Surface most relevant documents with metadata to easily drill down to a smaller, highly relevant set of results for a biomedical concept

Contact us to see a demo and learn more about tellic