Machine learning

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• Using large language models to extract PD-L1 testing details from electronic health records

  Poster presented at: ISPOR US 2024; May 5-8, 2024; Atlanta, GA

  • Real-world evidence
  • Publication summary
  • Publications
  • Machine learning
• Evaluation of real-world response rate in clinical trial-aligned cohorts of patients with lung, colon and breast cancer using machine learning

  Poster presented at: ISPOR US 2024; May 5-8, 2024; Atlanta, GA

  • Publication summary
  • Publications
  • Machine learning
  • Breast cancer
  • Colorectal cancer
  • Non-small cell lung cancer
• Using machine learning models to unlock insights into Castleman disease
  • Flatiron stories
  • Machine learning
  • Article
  • Technology & engineering
• A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

  Nature Communications

  • Publication summary
  • Publications
  • Machine learning
  • Methodology
• Enabling the clinical applications of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup

  JAMIA

  • Publication summary
  • Publications
  • Machine learning
• Scaling real-world data curation through machine learning and large language models
  • Real-world evidence
  • Machine learning
  • Article
  • Technology & engineering
• Debunking the myths of artificial intelligence in cancer care
  • Point of care
  • Event recap
  • Machine learning
  • Artificial intelligence
• Clinical characteristics, treatment trends, and outcomes of patients with HHV-8-negative/idiopathic multicentric Castleman disease treated with siltuximab

  2023 ASH Annual Meeting and Exposition

  • Publication summary
  • Publications
  • Machine learning
• Unlocking the patient journey: Comparing machine learning (ML) based natural language processing (NLP) and expert abstraction in understanding treatment patterns

  ICPE Annual Conference

  • Publication summary
  • Publications
  • Machine learning