Diabetic Medication Drug Recommender; The Deep-rooted History between South Asians and Diabetes a Case Study on Individualized Healthcare in America

Executive Summary
My STS Research Paper is a case study on the effects of individualized healthcare in the United States. The paper does a deep dive into the relationship between the actors in the United States healthcare system and looks specifically at the angle of a personalized healthcare test for people of different races. My technical work is on the prediction and predictors of diabetic medication depending on several factors. The work I am doing is still in progress, but it entails developing several machine learning algorithms and testing to see which ones give the best precision, f1, and accuracy scores. Both projects are healthcare related but are different in what they are trying to achieve. The research paper wants to see what changing individualized healthcare can do to the network that is already in place while the diabetes predictor is trying to create a personalized healthcare tool for those in need.
The Capstone project I am trying to create is a tool for healthcare professionals to use to possibly diagnose the best drug regimen for a diabetic patient. A binary relevance model is being used to create a multi-class output. The model is in the process of being deployed, and the UI to be created ideally in a form for ease of use. The other part of this capstone project is to find data on South Asian diabetes specifically as it is sparse and hard to find in large datasets. A comprehensive data cleaning and combination from different data sources is the goal.
The subject of my research paper is the current state of personalized healthcare
within America. My research question is, how do the changing relationships between actors’ impact individualized healthcare in the United States? I am using the ANT.
framework to support my argument. I expect to find a possible solution to a possible
disadvantage in the American Healthcare System. I expect to use a Case Study to see how the different actors in the network will adapt to the change of adding a new actor into the network or changing an actor's overall identity.
Working on these projects at the same time I have learned a great deal about the healthcare system in America. The projects reveal the complex networks that determine the type of care an individual deserves and the cost required to reach that level of care. The projects also reveal that the healthcare system is being burdened and is not as stable as it seems from the outside. Another thing I have learned is that data exists but is not organized well enough to be useful in a lot of cases. I have learned a great deal about how diabetes works and the mechanisms behind it, which are not just genetic. Working on these projects together has taught me a great deal about how careful we need to be when suggesting new ideas, and technologies as certain connotations may hurt a demographic more than helping it.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Nada Basit

STS Advisor: Bryn Seabrook

STS Advisor: Richard Jacques