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Adaptive Increment Learning for Estimating Optimal Individualized Treatment Regimes

來源:數學與統計學院          點擊:
報告人 朱文圣 教授 時間 8月23日9:30
地點 騰訊會議直播 報告時間

講座名稱:Adaptive Increment Learning for Estimating Optimal Individualized Treatment Regimes

講座時間:2020-08-23 9:30

講座人:朱文圣 教授

講座地點:騰訊會議直播(ID:880 164 821)

 

講座人介紹:

朱文圣,東北師范大學數學與統計學院教授、博士生導師、副院長。2006年12月博士畢業于東北師范大學,2013年12月起任東北師范大學數學與統計學院教授。2008-2010年在耶魯大學做博士后研究,2015-2017年訪問北卡羅來納大學教堂山分校。中國現場統計研究會計算統計分會副理事長、數據科學與人工智能分會秘書長,中國概率統計學會副秘書長,吉林省現場統計研究會秘書長等。主要從事統計學的方法與應用研究,在Journal of the American Statistical Association、Test、NeuroImage、中國科學等雜志發表學術論文多篇,主持并完成國家自然科學基金項目多項。

 

講座內容:

Personalized medicine has recently received increasing attention because of the significant heterogeneity of patient responses to the same medication. The estimation of individualized treatment regimes or individualized treatment rules is an important part of personalized medicine. Individualized treatment regimes are designed to recommend treatment decisions to patients based on their individual characteristics and to maximize the overall clinical benefit to the patients. However, most of the existing statistical methods are mainly focus on the estimation of optimal individualized decision rules for the two categories of treatment options and rely heavily on data from randomized controlled trials. There has been a relative lack of research work on the selection of multi-categorical treatment options in real-world settings. In this work, we address this challenge and propose a machine learning approach (AI-learning) to estimate optimal treatment regimes. This new learning approach allows for more accurate assessment of individual treatment response and alleviation of confounding. The increment value functions are proposed to compare matched pairs under a tree structure, and this approach allows for easy handling of the outcomes of various types of measuring treatment responses (including continuous, ordinal, and discrete outcomes). Through a large number of simulation studies, we demonstrate that AI-learning outperforms existing methods. Lastly, the proposed method is illustrated in an analysis of AIDS clinical trial data.

 

主辦單位:數學與統計學院

123

南校區地址:陜西省西安市西灃路興隆段266號

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