Phoenix's research
Presentation title: Anonymizing Health Data While Preserving Usefulness
Abstract: Our research project focuses on the importance of anonymity within patient data. We sorted time series data by grouping patient information using Python code. Then we clustered patients into groups using various methods to help identify existing patterns. After that, we added noise to the data and tracked how much the clusters had changed. This process is particularly helpful for tracking the amount of information needed to maintain patient anonymity while preserving useful data. This process can also predict a patient’s future data based on previous visits. Organizing information like this will improve personalized healthcare and ensure that patients aren’t easily identified.
