A University of Arkansas at Little Rock student who is graduating on Dec. 15 is making it easier for medical researchers to validate patient data found on social networks that can be used to make important decisions about what medical products are advanced for future development.

When Kim Tran of Little Rock was working at Arkansas Capital Corporation a decade ago, she noted that there was a statewide discussion on the critical importance of computer and information technology.

“I was working with business and government leaders throughout Arkansas who were talking about technology and how important it was to have access to the infrastructure in order to enable that technology,” she said. “At the time, people were also starting to talk about this thing called big data.

With this in mind, I wanted to learn more. UA Little Rock had also just partnered with MIT to develop a curriculum that was focused on the science of data and that is what brought me to UA Little Rock.”

Tran, who began the Ph.D. program in computer and information science in 2010 as a part-time student who worked full time, said one of the most challenging aspects of the process was selecting a topic for her dissertation, citing the more than 1,200 articles she reviewed before choosing a topic. She’s grateful for her professors who served as mentors during her time at UA Little Rock.

“Dr. Rolf Wigand was always pushing the boundary for me,” she said. “Every time I felt good about where I was at, he would challenge me to look around the next corner. Ph.D. students need this kind of feedback in order to strengthen the quality of their research. Dr. John Talburt and Dr. Meredith Zozus especially helped me contextualize my research. I also developed lifetime friendships with many professors at the university. They were an exceptionally supportive group, and I was lucky to have that.”

Having a support structure during her doctoral endeavors was something she especially owes to her dissertation advisor, Dr. Nitin Agarwal, Maulden-Entergy Endowed Chair and distinguished professor in the Department of Information Science and director of the Collaboratorium of Social Media and Online Behavioral Studies (COSMOS).

“The great thing about the Ph.D. process is that you have an advisor who will guide you through the process and help open doors so that you can grow and develop. Dr. Agarwal guided me through the process,” she said.

Tran’s research brings together the fields of machine learning and natural language processing, psychometrics, and social networks, all of which are applied to Idiopathic Pulmonary Fibrosis (IPF), a lung disease which results in scarring (fibrosis) of the lungs for unknown reasons. An estimated five million patients worldwide and 150,000 patients in the United States are affected by this disease.

“Kim’s research bridges the disciplines of statistics, health sciences, information sciences, and social networks by developing a computational framework to assess social media’s validity in capturing patient reported outcomes from Idiopathic Pulmonary Fibrosis patients,” Agarwal said. “Her research has far-reaching implications to the health domain by facilitating exploratory efforts in the medical product development process.”

Since 2009, regulatory reviewers have been looking at ways to incorporate patient input into its drug selection process, in order to bring drugs to the market sooner, Tran said. In 2015, a discussion held between regulatory reviewers, pharmaceutical companies, and a patient group generated consensus on the potential of social networks in supporting the validity of patient outcomes identified for medical product development and her dissertation creates a scalable framework from which the validity of social networks can be determined.

“Healthcare is very unique domain since research in this area affects the lives of patients,” she said. “So any data you are deriving from any source will require a high level of scrutinization. Social networks are one possible platform that can be used as a source to develop patient-reported outcomes. While the ideal source of feedback is obtained directly from the patient, the way in which this information is gathered is highly variant in scope and in quality. The FDA, for example, still collects patient input through town halls. In the search for more efficient methods of gathering patient understanding, social networks serve as a unique source of observational data.”

In order to study whether the data is valid, she uses advanced probabilistic methods to analyze and evaluate Idiopathic Pulmonary Fibrosis messages from Twitter for the last 10 years across 34 different languages from around the world.

Tran was drawn to study Idiopathic Pulmonary Fibrosis after attending an international research conference where she spoke with patients and about this little known disease.

“IPF is not as well known or studied as breast cancer,” she said. “When something is idiopathic, you don’t know the origin. The one thing you do know is that your disease is fatal and that it will result in markedly reduced lung capacity over time. I met with and spoke at length with many patients who were affected by this disease at an international conference. It was eye opening and also touching how driven and motivated these patients were to learn about IPF. They were there because they didn’t want to just be a patient, they wanted to be a part of finding a cure. That gave me the drive to learn more about the field and to help advance the understanding of idiopathic pulmonary fibrosis.”

As part of her dissertation, Tran has collaborate with a researchers across the country who are planning to set up additional studies based on this research.

“I have been fortunate to meet researchers from other institutions that I have been working with as well as others that I will begin to work with,” she said. “This is a group which will bring diverse perspectives and includes researchers from UAMS, Yale, Georgetown, Northeastern, and Tulane University. There is much opportunity to extend this research to fully evaluate the validity of social networks, and I am really looking forward to it.”

In the end, Tran is grateful for the opportunities that earning a Ph.D. brought her.

“The Ph.D. process is an excellent development opportunity as long as you are able to commit to the process,” Tran said. “Through this process, you learn how to learn. I had an opportunity to work across a variety of fields that are all on the cutting edge of things that matter in today’s business environment and to make a novel contribution to the field.”