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果盘游戏:Advancing Responsible AI/ML for Harnessing the Power of Electronic Health Records

来源: 曾敏 点击: 时间:2024年12月30日 20:14

时间:202513日下午4

点:校区信息楼416会议

报告人: 龙琦

介:

琦,现任美国宾夕法尼亚大学医果盘游戏生物统计学教授,工程果盘游戏计算机学教授,沃顿商果盘游戏统计学教授。同时兼任生物统计,流行。靶畔⒀迪蹈敝魅危话┲⑹菘蒲е行拇词贾魅危话┲⒀芯吭褐鞴苁菘蒲Ш腿斯ぶ悄艿母痹撼ぃ簧镆窖畔⒀芯吭焊痹撼。 当选美国科学促进会会士美国统计学会会士, 国际统计学会会士,以及美国医学信息学会会士。长期从事与精准医疗和健康有关的统计学, 信息学, 机器学习, 和人工智能的研究, 包括医疗大数据分析和推断(多组学数据, 电子病历数据, 影像数据等), 缺损数据, 因果分析, 贝叶斯方法, 临床实验,数据隐私,算法公平,以及大语言模型等方向。发表的杂志包括国际知名期刊Nature Medicine, Nature Communications, JAMA Oncology, Cancer Research, PNAS, 统计学顶刊Annals of Statistics, Journal of the American Statistical Association, 以及AI/ML顶会 ICML, NeurIPS。


报告摘要:

Rich electronic health records (EHR) data offer remarkable opportunities in advancing precision medicine, they also present daunting analytical challenges. Multi-modal data in EHR that are recorded at irregular time intervals with varying frequencies include structured data such as labs and vitals, codified data such as diagnosis and procedure codes, and unstructured data such as clinical notes and pathology reports. They are typically incomplete and fraught with other data errors and biases. What’s more, data gaps and errors in EHRs are often unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete data in EHRs in United States. Such data bias, if not adequately addressed, would lead to biased results and exacerbate health inequities. In this talk, I will share my research group’s recent works on responsible AI/ML models including large language models (LLMs) for addressing these challenges. Since LLMs are themselves plagued by various biases, I will also briefly discuss our ongoing research on developing rigorous statistical and ML methods for mitigating pitfalls and risks of LLMs.


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