数据科学日益增长的多样性:来自临床试验的共享经验教训

R. Simari
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引用次数: 0

摘要

西方社会的人口结构正在迅速变化。在美国,人口正在老龄化,并且变得越来越多样化(1)。明年,在18岁以下的人群中,种族将不再占多数。到2060年,在整个美国人口中,女性将不再占多数。这些变化的影响是巨大的,学术事业也不会幸免。在这些正在进行的社会变革中,学术界的工作和学术界的劳动力将永远改变。数据科学有可能改变生物医学的基本框架。机器学习和人工智能有能力识别可能导致疾病预防和治疗创新的机制和关联。然而,数据科学必须随着正在进行的社会变革而发展。在
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Growing Diversity in Data Science: Shared Lessons from Clinical Trials
he demographic nature of western society is rapidly changing. In the United States the population is aging and becomingly increasingly diverse (1). Next year, there will not be a majority race among those under 18. By 2060 there will be no majority within the entire US population. The implications of these changes are enormous and the academic enterprise will not be spared. The work of academia and the work force of academia will be forever changed within these ongoing social changes. Data science has the potential to alter the fundamental framework of biomedicine. Machine learning and artificial intelligence have the capacity to identify mechanisms and associations that may lead to innovations in disease prevention and therapy. Yet data science must evolve with the social changes underway. In
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