3917 1624
KK 815
Haipeng Shen joined HKU in September 2015 as a Professor of Innovation and Information Management at the Faculty of Business and Economics (now HKU Business School). Before joining HKU, he was a Professor of Statistics and Operations Research, University of North Carolina at Chapel Hill, USA.
Business Analytics, Business Data Analysis, Decision and Risk Analysis, Statistics
Data-driven decision making in the face of uncertainty: big data, business analytics, healthcare analytics, service engineering.
以時間為單位量度客戶的耐性並計算危機率，已成為運營管理中重要的一環。這項技術能幫助企業優化客戶服務中心的運作，如人手分配及編更事宜等等。當客戶申請服務時，他們的耐性將會隨時間推移產生改變。現行的數據收集系統有時候無法觀察到客戶服務中心提供服務的確切時間，故此我們開發了一個TunT（Transform-unTransform）的估計程式，把這個複雜的難題簡化為迴歸分析的問題。我們為客戶服務中心不同的服務時間進行分類，並使用均值匹配轉換，對相關的數據進行轉換，從而使我們能簡單地表現出異方差－變異數函數。然後我們把非參數迴歸分析的技術應用在轉換後的數據上。最後，我們會對估計出來的迴歸函數進行轉換，計算出原始危機率。在我們的模擬計算中，我們利用客戶服務中心的數據（實驗組）與醫療保險計劃中的數據（控制組）進行對比。研究證明，與現行方法相比，我們的模型能得出更準確的危機估計值，幫助企業優化人手安排。
Innovation in healthcare is forever changing how we see and experience the medical industry. The environment is offering HKU’s Faculty of Business and Economics (the Faculty) a unique opportunity to be at the forefront of utilising rich data, creating better health outcomes for everyone.
Patients in China suffering from acute ischemic stroke, when arteries leading to the brain are blocked, have traditionally not experienced excellent clinical outcomes. Battling this disease has been a long-term battle for physicians working in the country’s overcrowded under-resourced public hospitals. Professor Haipeng Shen, Patrick S C Poon Professor in Analytics and Innovation at HKU Business School, has been working to change this situation by collaborating with top physicians and embracing the power of big data.
We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.
Professor Haipeng SHEN, Patrick S C Poon Professor in Analytics and Innovation, was interviewed by four media outlets on his recent research in data-driven decision making in healthcare management.
