A look back on my PhD…

I was recently interviewed by my fellow ISP student, Huihui Xu, about my experience with the Intelligent Systems Program at Pitt. Huihui served as the editor of 2019 Intelligent Systems Program Newsletter. With her permission, I am posting an adapted version of her article here. I started this blog during the first week of my PhD program. I reflect on my journey in this post.

On how I picked my dissertation topic and why I think it was important…

While preparing my statement of purpose for the PhD program, I had plans to work on AI systems that work in collaboration with human experts. I was interested in Human-Computer Interaction and Intelligent Interfaces in general at that time.

I had an opportunity to join a project team with Drs. Hochheiser (my PhD advisor), Wiebe, Hwa, and Chapman during the first year of my program. Later, members from this project also served on my committee.

We explored methods for incorporating clinician (human) feedback to build Natural Language Processing models. The project helped me form the core idea of my dissertation: Interactive Natural Language processing for Clinical Text.

Current approaches require a long collaboration between clinicians and data-scientists. Clinicians provide annotations and training data, while data-scientists build the models. The domain experts do not have provisions to inspect these models or give direct feedback. This forms a barrier to NLP adoption limiting its power and utility for real-world clinical applications.

In my dissertation "Interactive Natural Language Processing for Clinical Text" (Trivedi, 2019), I explored interactive methods to allow clinicians without machine learning experience to build NLP models on their own. This approach may make it feasible to extract understanding from unstructured text in patient records; classifying documents against clinical concepts, summarizing records and other sophisticated NLP tasks while reducing the need for prior annotations and training data upfront.

On obstacles to my dissertation

One challenge I faced during the middle of my dissertation was identifying further clinical problems (and data) where I could replicate the ideas defined in my first project.

Pursuing my PhD program in the Intelligent Systems Program allowed me to form good collaborations at Department of Biomedical Informatics, with Dr. Visweswaran’s group, as well as clinicians from University of Pittsburgh Medical Center. Dr. Handzel, who is a Trauma surgeon, served as a teaching assistant for my Applied Clinical Informatics course at DBMI. I was able to discuss my ideas for insights on clinical problems that I could work on. He also got on board to develop the ideas further. We worked on building an interactive tool for "Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports" (Trivedi et.al., 2019):

During initial validation of my ideas, I even had a chance to shadow trauma surgeons in the ICU. These collaborations not only made it easier to get access to the required data, but also run my evaluation studies with physicians as study participants.

On Intelligent Systems Program

ISP is an excellent program for applied Artificial Intelligence. The founders were definitely visionaries in starting a program dedicated for AI applications over thirty years ago. Now everybody is talking about using machine learning (and more recently deep learning) for applications in medicine & health, education and law. ISP provides an environment for interdisciplinary collaboration. Clearly, I benefited a lot from these collaborations for my dissertation.

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  1. Trivedi (2019), Interactive Natural Language Processing for Clinical Text. Available: http://d-scholarship.pitt.edu/37242/.
  2. Trivedi et.al. (2019), Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports. Available: https://doi.org/10.1055/s-0039-1695791.