Chieh Lee

Colorectal cancer is the leading cause of cancer-related deaths worldwide, and early detection has proven to be an effective method for reducing mortality. The machine learning method can be implemented to build a noninvasive stratifying tool that helps identify patients with potential colorectal precancerous lesions (polyps). This study aimed to develop a noninvasive risk-stratified tool for colorectal polyps in asymptomatic, healthy participants. A total of 20,129 consecutive asymptomatic patients who underwent a health checkup between January 2005 and August 2007 were recruited. Positive relationships between noninvasive risk factors, such as age, Helicobacter pylori infection, hypertension, gallbladder polyps/stone, and BMI and colorectal polyps were observed (p < 0.0001), regardless of sex, whereas significant findings were noted in men with tooth disease (p = 0.0053). A risk stratification tool was developed, for colorectal polyps, that considers annual checkup results from noninvasive examinations. For the noninvasive stratified tool, the area under the receiver operating characteristic curve (AUC) of obese females (males) aged 50 years old), the AUCs of the stratifying tools were >85%. Our results indicate that the risk stratification tool can be built by using random forest and serve as an efficient noninvasive tool to identify patients requiring colonoscopy.

As supply chains span across one or more continents, an intermediate firm becomes an inevitable supply chain component. While seller-financing options (e.g. delay in payment), are well studied, researchers have rarely investigated them from the perspective of an intermediate firm in a supply chain. We construct a three-echelon supply chain that consists of a supplier, distributor, and retailer. In our model, the supplier and distributor may offer seller financing to their buyer, but only the distributor is subject to the buyer's default risk. We consider a seller-financing supply chain wherein either all or none of the sellers premise delay in payment and a special case where the distributor may coordinate with the supplier or retailer. An algorithm solves optimal replenishment and delay-in-payment decisions. Numerical analyses illustrate managerial insights, which show that when the retailer's default risk is high, the distributor's best strategy is not to offer any delay-in-payment. When the distributor-retailer coordination can subsidise the retailer's default risk, the distributor's best strategy is coordination with the retailer and bargaining for more financial support from the supplier. In contrast, as the retailer's default risk is inevitable, the distributor-supplier integration is the optimal supply chain financing strategy.