Natural language processing-based detection of transgender and gender non-conforming patients in electronic health record-derived data Published May 2021 Citation Hooley, IJ, Maignan, K, Ngai, D, Ackerman, B. . ISPOR Annual Meeting. . https://www.ispor.org/heor-resources/presentations-database/presentation/intl2021-3338/109635 Authors:Hooley, IJ, Maignan, K, Ngai, D, Ackerman, B Sources:ISPOR Annual Meeting Share Posted inPublicationsMachine learningHealth equity 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