Machine learning

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• Using large language models for scalable extraction of real-world progression events across multiple cancer types

  AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning

  • Real-world evidence
  • Publication summary
  • Publications
  • Data science
  • Machine learning
  • Artificial intelligence
• Responsible AI in action: Flatiron's approach to AI fairness and bias assessment
  • Real-world evidence
  • Machine learning
  • Quality improvement
  • Methodology
  • Artificial intelligence
• Ensuring reliability of curated EHR-derived data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework

  arXiv

  • Publication summary
  • Publications
  • Machine learning
  • Quality improvement
  • Quality reporting
• A geyser of biomarkers: Extracting data from NGS reports
  • Flatiron stories
  • Machine learning
• Concordance of response-based clinical trial and machine learning–generated real-world end points

  ASCO Annual Meeting

  • Publication summary
  • Publications
  • Machine learning
  • Non-small cell lung cancer
• Leveraging machine learning to assess the association of rash and survival in patients with advanced NSCLC

  ISPOR

  • Real-world evidence
  • Publication summary
  • Publications
  • Machine learning
• Performance assessment and validation of real-world response data generated using a deep learning-based natural language processing model across multiple solid tumors

  ISPOR

  • Real-world evidence
  • Publication summary
  • Publications
  • Machine learning
• Advancing breast cancer research in Japan using EHR-derived real-world data

  ISPOR

  • Real-world evidence
  • Publication summary
  • Publications
  • Machine learning
• Patient characteristics and treatment patterns in extensive-stage small cell lung cancer (ES-SCLC)

  ISPOR

  • Publication summary
  • Publications
  • Machine learning
  • Small cell lung cancer