Temporally integrated framework for treatment intervals (TIFTI): A framework for extracting drug intervals from longitudinal clinic notes Published December 2018 Citation Agrawal, M, Adams, G, Nussbaum, NC, Birnbaum, B. . Machine Learning for Health (ML4H) Workshop. . https://arxiv.org/pdf/1811.12793.pdf Authors:Agrawal, M, Adams, G, Nussbaum, NC, Birnbaum, B Sources:Machine Learning for Health (ML4H) Workshop Share Posted inPublicationsMachine learningMethodology More publicationsAACR Special Conference in Cancer Research: Artificial Intelligence and Machine LearningJuly 2025Using large language models for scalable extraction of real-world progression events across multiple cancer typesCohen A, Krismer K, Magee K, et al. Real-world evidencePublication summaryPublicationsData scienceMachine learningArtificial intelligencearXivJune 2025Ensuring reliability of curated EHR-derived data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) FrameworkEstevez M, Singh N, Dyson L, et al. Publication summaryPublicationsMachine learningQuality improvementQuality reportingASCO Annual MeetingMay 2025Concordance of response-based clinical trial and machine learning–generated real-world end pointsZhang Q, Krismer K, Lu Y, et al.Publication summaryPublicationsMachine learningNon-small cell lung cancer