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Biomedical Question Answering

Combine information extracted from medical research, clinical trials, ontologies, and other sources into a knowledge graph – and answer natural language questions about the content.
Extract

Entities from unstructured text with NLP

Connect

Information to form a graph

Visualize & Explore & Discover

Facts from the knowledge graph

Roche Biomedical
Knowledge Graph
  • 10k+ relation types / 500k clinical trials / 85M entities / 260M facts / 5B+ additional facts
  • Build at scale using named entity extraction
  • Supports adding private facts into the graph
  • Tested on BioASQ challenge

Biomedical Q&A at Roche

  • 10k+ Predicates (Relation types)
  • 500k Clinical Trials
  • 85M Entities
  • 260M Extracted Facts from Text
  • 5B+ Total Facts
  • The end-to-end system includes a user interface for graph visualization, exploration, search, and question answering
  • Knowledge graph built at scale, combining multiple free-text data sources and ontologies
  • Supports adding private documents and facts to the graph
  • Tested on BioASQ challenge

The Clinical Knowledge Graph

Combining Spark NLP & Neo4j
  • Extracted named entities & relations between from
  • Typical questions:
  • What are the drug interactions and adverse drug events of “Lipitor”?
  • What are the routes by which the drug containing API “Rofecoxib” is administered?
Automated question answering about clinical guidelines
  • Joint work with Kaiser Permanente
  • Answering both specific and general questions (‘how to treat diabetes?’)
  • Automatic identification documents or section of documents with corresponding clinical guidelines – from curated set of clinical guidelines
  • Indexing free text documents including PDF and DOCX
  • Scalable to a large number of documents.