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 publicationsASCO Annual MeetingMay 2026From plenary to practice: A large language model (LLM)-based thematic analysis of landmark clinical trial discussions and factors influencing their real-world adoptionCohen AB, Williams T, Aggarwal C, et al.Publication summaryPublicationsMachine learningArtificial intelligenceASCO Annual MeetingMay 2026Using ML to predict rapid progression for patients (pts) with HR+/HER2- metastatic breast cancer (mBC) treated with frontline (1L) CDK 4/6 inhibitors (CDK 4/6i)Peng M, Rios G, Estevez M, et al.Publication summaryPublicationsPrecision medicineMachine learningMetastatic breast cancerArtificial intelligenceASCO Annual MeetingMay 2026Machine learning risk stratification in a US-based database to identify subgroups of patients with PD-L1-high NSCLC who benefit from adding chemotherapy to pembrolizumabOrcutt X, Nimgaonkar V, Sun L, et al.Publication summaryPublicationsMachine learningComparative effectivenessNon-small cell lung cancerArtificial intelligence