Our International Research Team including:


Prof. Tetsuya Sakurai
University of Tsukuba, Japan
Prof. Su-Hsin Chang
Washington University in St. Louis, United States
Dr. Chalong Cheewakriangkrai
Chiang Mai University, Thailand
Prof. Quan Zou
Tianjin University, China
Prof. Yang Xu
Peking University, Beijing, China
Prof. Chung-Li Tseng
University of New South Wales in Sydney, Australia
Prof. Asit Kumar Das
Bengal Engineering and Science University, India
Prof. Yen-Wei Chu
National Chung Hsing University, Taiwan

Research

1. Multiple Primary Malignant Neoplasms (MPMNs)

The high effectiveness of cancer screening and therapies resulted in the increased diagnosis of multiple primary malignancies (MPMNs) in the world. The topic of this study was to investigate the clinical data of patients and to determine the frequency and clinical features of MPMNs.

2. Recurrent Cancer

The relationship between prognosis and cancer has been extensively investigated, yet its impact on recurrence and cancer patients has not been well studied. The early prediction of recurrence has become a necessity in cancer research. The objective of this topic is to develop an evidence-based diagnostic model with advanced machine learning techniques for the prediction of risk factors of recurrent cancer.

3. Shared Medical Decision Making

In recent years, providers of health care have moved away from a paternalistic approach to one that actively encourages patient autonomy and participation in treatment decision making. Evidence based patient choice seems based on a strong liberal individualist interpretation of patient autonomy; however, not all patients are in favor of such an interpretation. The purpose of this study was to investigate the changing dynamics of modern health care combined with ethical considerations have lent impetus to studies that attempt to explain the foundations of patient decision making.

4. Decision Analysis & Simulation Modeling

The effective management of uncertainty is one of the most fundamental problems in medical decision-making. Currently, most medical decision models rely on point estimates for input parameters, although the uncertainty surrounding these values is well recognized. It is natural that the physician should be interested in the relationship between changes in those values and subsequent changes in model output. Bayesian decision analysis provides a means of quantifying subjective judgments and combining them in a rigorous way with information obtained from clinical data. Instead of considering only the sparse data, Bayesian analysis can provide a technique by which prior knowledge, such as expert opinion, past experience, or similar situations, can be taken into account. One area in which further work might be desirable is in the study of other failure models using the same procedure developed in this topic.

5. Performance Measurements in Health Services

With the change in medical environment, the healthcare providers can only survive if they strengthen the quality of their services and promote patient satisfaction. In this topic, we focus on patient satisfaction and operational performance model for institutions to be used to access the operational management of themselves. Some techniques, for example, the Analytic Hierarchy Process (AHP) to evaluate the degree of customer satisfaction, and Data Envelopment Analysis (DEA), which uses linear programming to measure the relative performance of institutions.

6. Clinical Psychology

In general, there are two main goals of cancer treatment: the first is to cure the malignancy or lengthen survival time, and the second is to improve quality of life (QoL). Until recently, posttraumatic growth (PTG) has been increasingly studied during the past decade, yet it has been unexplored in the research field of cancer. Although progress has been made in understanding positive outcomes following cancer diagnosis and treatment, the literature has been characterized by several methodological limitations. This topic has several objectives: (i) to examine when change in PTG symptoms occurs at a patient level over a long period by testing different hypotheses of change, and (ii) to determine the respective predictive power of patients' involvement, trust, decisional conflict and personality for developing short and long-term PTG symptoms, and (iii) explore the association between PTG and QoL.