Research on Financial Web Sites Designed for Teenagers (Fall 2001) IPRO 359

The proliferation of the Internet in recent years has led to more people using, and being confused by, computer interfaces. Usability testing is a formal laboratory procedure used to identify problems with user interfaces during the development of a product. It involves creating tasks for users to accomplish and watching whether they can complete the given tasks. More importantly, the testers observe how users interact with the interface in an attempt to understand why they perform the actions they do. The testing allows developers to identify issues with their designs early enough in the product development cycle to be able to change them, saving money and resources later, in addition to creating an easier-to-use product. Unfortunately, many companies do not implement usability testing or do so too late into development to make any substantive changes suggested by the testing. The result is often a product that is needlessly hard to use. The team will conduct usability tests during the semester to gain first-hand experience in all aspects of usability testing. This project will test the interfaces of products produced by other IPROs and will work closely with other teams to identify and address issues arising from their work so that students on this team and others can appreciate the value of usability testing and designing with the end users in mind. A major focus of the team's work during the Fall 2001 semester will be to develop a business plan for the IIT Usability Testing and Evaluation Center (UTEC), and in particular to develop a marketing strategy for establishing UTEC as the Underwriter's Laboratory of the internet, whereby a web site authorized by UTEC to display the (UTEC) seal of approval will hold the distinction of having met minimum standards of usability for its intended user populations.Sponsorship: NAProject Plan for IPRO 359: Research on Financial Web Sites Designed for Teenagers for the Fall 2001 semeste

A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study

Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.OAIID:RECH_ACHV_DSTSH_NO:T201713729RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A079841CITE_RATE:4.259FILENAME:Yoo_et_al-2017-Scientific_Reports (1).pdfDEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/4114e6b2-67f4-4a34-8eae-66d310499888/linkY