光華講壇——社會名流與企業(yè)家論壇第6681期

主題：Estimating Patient Health Transition from Data Censored by Treatment-Effect-Based Policies從基于治療效果的政策所刪減的數(shù)據(jù)中估算患者的健康狀況轉(zhuǎn)變

主講人：鄭智超

主持人：肖輝

時間：12月12日 10:00-12:00

舉辦地點(diǎn)：西南財經(jīng)大學(xué)柳林校區(qū)通博樓D301

主辦單位：西南財經(jīng)大學(xué)管科學(xué)院 科研處

主講人簡介：

Zhichao Zheng is an Associate Professor of Operations Management at the Singapore Management University. His main research interests lie in data analytics and optimization for healthcare operations management and medical decision-making. He also applies his research in sharing economics, supply chain risk management, etc. His research has appeared in Operations Research, Management Science, and Manufacturing & Service Operations Management, among others. He received his BS (First Class Honors) in Applied Mathematics from the National University of Singapore in 2009 and Ph.D. in Management from the Department of Decision Sciences (renamed to Department of Analytics & Operations) at the National University of Singapore in 2013.鄭智超是新加坡管理大學(xué)運(yùn)營管理教授。他的主要研究興趣是醫(yī)療運(yùn)營管理和醫(yī)療決策的數(shù)據(jù)分析與優(yōu)化。他還將研究成果應(yīng)用于共享經(jīng)濟(jì)學(xué)、供應(yīng)鏈風(fēng)險管理等領(lǐng)域。他的研究成果發(fā)表在《運(yùn)營研究》、《管理科學(xué)》、《制造與服務(wù)運(yùn)營管理》等雜志上。他于2009年獲得新加坡國立大學(xué)應(yīng)用數(shù)學(xué)學(xué)士學(xué)位（一等榮譽(yù)學(xué)位），并于2013年獲得新加坡國立大學(xué)決策科學(xué)系（已更名為分析與運(yùn)營系）管理學(xué)博士學(xué)位。

內(nèi)容簡介：

Treatment-effect-based decision policies are increasingly used in healthcare problems. Such policies leverage predictive information on patient health transitions and treatment outcomes for treatment recommendations. However, these policies can significantly censor the observation of patients’ health transitions and distort the estimation of transition probability matrices (TPMs). We propose a structural model to recover the underlying true TPMs from censored transition observations. We show that the estimated TPM from the structural model is consistent and asymptotically normally distributed and also maximizes the log-likelihood of observing the data. Using hypothesized data with known ground truth TPMs, we demonstrate the advantages of our model against benchmark estimation methods that ignore the censoring mechanism. We further implement our model to estimate patient health transitions using observed data for the extubation problems in an intensive care unit (ICU). Formulating the extubation problem as a classical optimal stopping Markov Decision Process model, we show that the proposed method, with more accurate estimated TPMs considering treatment-effect-based policy censoring, can reduce patients’ length of stay in the ICU compared to benchmark methods.基于治療效果的決策政策越來越多地應(yīng)用于醫(yī)療保健問題。此類政策利用患者健康狀況轉(zhuǎn)變和治療結(jié)果的預(yù)測信息來提出治療建議。然而，這些政策會嚴(yán)重刪減對患者健康轉(zhuǎn)變的觀察，并扭曲對轉(zhuǎn)變概率矩陣（TPM）的估計。我們提出了一個結(jié)構(gòu)模型，以從剔除的過渡觀測中恢復(fù)潛在的真實(shí)TPM。我們的研究表明，從結(jié)構(gòu)模型中估算出的TPM是一致的、漸近正態(tài)分布的，而且能使觀測數(shù)據(jù)的對數(shù)似然最大化。通過使用具有已知基本事實(shí)TPM的假設(shè)數(shù)據(jù)，我們證明了我們的模型與忽略刪減機(jī)制的基準(zhǔn)估計方法相比所具有的優(yōu)勢。我們進(jìn)一步使用我們的模型，利用觀察到的重癥監(jiān)護(hù)室（ICU）拔管問題數(shù)據(jù)來估計病人的健康狀況轉(zhuǎn)變。我們將拔管問題表述為一個經(jīng)典的最優(yōu)停止馬爾可夫決策過程模型，結(jié)果表明，與基準(zhǔn)方法相比，考慮到基于治療效果的政策剔除，所提出的方法具有更精確的估計TPM，可以縮短患者在重癥監(jiān)護(hù)室的住院時間。