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The ability to predict the outcome of future biomedical research has significant scientific, clinical, and business benefit. For example, one might want to predict:
  • Mutations in a fully-sequenced exome that will be found responsible for a particular problematic phenotype
  • Cancer patients who will respond to a particular drug
  • Success of a specific clinical trial
Parity's Literature-Based Hypothesis Discovery (LBHD) technology uses a proprietary combination of biomedical data mining, Natural Language Processing (NLP), and machine learning, optimized for accurate prediction of future research results using only data available prior to the research.

In response to queries, Parity’s LBHD automatically mines the entirety of the peer-reviewed biomedical literature and identifies meaningful indirect connections among concepts (genes, proteins, diseases, phenotypes, etc.) in order to rank potential answers to important research and clinical questions.

Parity has also applied proprietary machine learning, deep NLP, and real-time physiological signal analytics to create high accuracy predictive models for clinical decision support. For example, this platform yielded industry-leading specificity and timeliness of alerts for patients at risk of sepsis in the hospital setting

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