Computationally Enhanced Medical Decision Support for Pancreatic Cancer

This project investigated and applied computational techniques to enlarge a pancreatic cancer database and to enhance the medical decision-making process supported by this database. The database was previously developed by the Department of Surgical Oncology of the University of Massachusetts Medical School in conjunction with the Department of Computer Science at WPI. We substantially increased the number of patients included in the database, and conducted data mining experiments. These experiments compared the accuracies of predictions made by medical doctors and by data mining methods for two separate patient outcomes: tumor malignancy and survival time after surgery. The results of our experiments show that data mining techniques can be used to enhance the quality of medical decisions

An Activity Monitor for Diabetic Individuals

An activity monitor that diabetic individuals can wear continuously will provide important information on how these individuals should make adjustments to their exercise, diet, and insulin dosage in order to maintain a healthy lifestyle. The device is composed of both heart rate sensing components and components to measure the magnitude of physical movement. The energy expenditure is calculated using an algorithm that continuously adjusts depending on the type of activity. The system display provides the carbohydrates burned in order to be adjunctive to carbohydrate counting, a common technique used for glucose management