Incorporation of Machine Learning Tools to Predict Global Outcomes for Patients with Relapsed and Refractory Peripheral T and NK/T-Cell Lymphomas in the Contemporary Era

Boussi, Leora; Koh, Min Jung; Han, Xinyi; Peng, Luke; Koh, Min Ji; Eche, Ijeoma Julie; Ford, Josie Germain; Singh, Shambhavi; Miranda, Eliana Cristina Martins; Chiattone, Carlos S.; Weyden, Carrie van der; Prince, H. Miles; Foss, Francine M.; Yoon, Sang Eun; Kim, Won-Seog; Panchoo, Girisha; Verburgh, Estelle; Alturas, Jackielyn Cuenca; Al‐Μansour, Mubarak; Manni, Martina; Federico, Massimo; Cabrera, María Elena; Casadei, Beatrice; Zinzani, Pier Luigi; Yoshida, Noriaki; Okatani, Takeshi; Merrill, Mwanasha; Jacobsen, Eric; O’Connor, Owen A.; Marchi, Enrica; Jain, Salvia

doi:10.1182/blood-2022-162944

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2024

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“…To capture this underlying causal relationship, we have developed a novel causal survival method, survival synthetic intervention (SSI), to provide accurate survival analysis using real-world clinical data (Koh et al 2023;Boussi et al, 2022). This simulator enables us to estimate personalized outcomes based on specific patient characteristics and treatments.…”

Section: Resultsmentioning

confidence: 99%

Advancing Equality: Harnessing Generative AI to Combat Systemic Racism

Gabriel,

Han,

Liu

et al. 2024

An MIT Exploration of Generative AI

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Systemic racism, an issue deeply entrenched in various sectors of American society, continues to cast a shadow over the nation's progress towards equality and justice. This paper showcases some of generative artificial intelligence (AI) potential to identify racial bias and combat structural barriers that perpetuate racial discrimination in the United States. Specifically, three research groups affiliated with MIT's Initiative on Combatting Systemic Racism (ICSR) present case study applications that utilize generative AI in innovative approaches against the structural foundations of racism in policing, healthcare, and housing.

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Section: Resultsmentioning

confidence: 99%