Gaurav Trivedi, Phuong Pham, Wendy W Chapman, Rebecca Hwa, Janyce Wiebe, Harry Hochheiser; NLPReViz: an interactive tool for natural language processing on clinical text. Journal of American Medical Informatics Association. 2017. DOI: 10.1093/jamia/ocx070.
Update: We received the best student paper award for our paper at JURIX’15!
In an earlier post, I talked about my work on Natural Language Processing in the clinical domain. The main idea behind the project is to enable domain experts to build machine learning models for analyzing text. We do this by designing usable tools for NLP without really having the need to send datasets to machine learning experts or understanding the inner working details of the algorithms. The post also features a demo video of the prototype tool that we have built.
I was presenting this work at my program’s bi-weekly meetings where Jaromir, a fellow ISP graduate student, pointed out that such an approach could be useful for his work as well. Jaromir also holds a degree in Law and works on building AI systems for legal applications. As a result, we ended up collaborating on a project on using the approach for statutory analysis. While, the main topic of discussion in the project is on the framework in which a human experts cooperate with a machine learning text classification algorithm, we also ended up augmenting our approach with a new way of capturing and re-using knowledge. In our tool datasets and models are treated separately and our not tied together. So, if you were building a classification model for say statutes from the state of Alaska, when you need to analyze laws from Kansas you need not start from scratch. This allows us to be in a better starting place in terms of all the performance measures and build a model using fewer training examples.
Jaromír Šavelka, Gaurav Trivedi, and Kevin Ashley. 2015. Applying an Interactive Machine Learning Approach to Statutory Analysis. In Proceedings of the 28th International Conference on Legal Knowledge and Information Systems (JURIX ’15). Braga, Portugal. [PDF] – Awarded the Best Student Paper (Top 0.01%).
Update: Here’s our full paper announcement with source-code release…
I am working on a project to support the use of Natural Language Processing in the clinical domain. Modern NLP systems often make use of machine learning techniques. However, physicians and other clinicians, who are interested in analyzing clinical records, may be unfamiliar with these methods. Our project aims to enable such domain experts make use of Natural Language Processing using a point-and-click interface . It combines novel text-visualizations to help its users make sense of NLP results, revise models and understand changes between revisions. It allows them to make any necessary corrections to computed results, thus forming a feedback loop and helping improve the accuracy of the models.
Here’s the walk-through video of the prototype tool that we have built:
At this point we are redesigning some portions of our tool based on feedback from a formative user study with physicians and clinical researchers. Our next step would be to conduct an empirical evaluation of the tool to test our hypotheses about its design goals.
Gaurav Trivedi. 2015. Clinical Text Analysis Using Interactive Natural Language Processing. In Proceedings of the 20th International Conference on Intelligent User Interfaces Companion (IUI Companion ’15). ACM, New York, NY, USA, 113-116. DOI 10.1145/2732158.2732162 [Presentation] [PDF]
Gaurav Trivedi, Phuong Pham, Wendy Chapman, Rebecca Hwa, Janyce Wiebe, Harry Hochheiser. 2015. An Interactive Tool for Natural Language Processing on Clinical Text. Presented at 4th Workshop on Visual Text Analytics (IUI TextVis 2015), Atlanta. http://vialab.science.uoit.ca/textvis2015/ [PDF]
Gaurav Trivedi, Phuong Pham, Wendy Chapman, Rebecca Hwa, Janyce Wiebe, and Harry Hochheiser. 2015. Bridging the Natural Language Processing Gap: An Interactive Clinical Text Review Tool. Poster presented at the 2015 AMIA Summit on Clinical Research Informatics (CRI 2015). San Francisco. March 2015. [Poster][Abstract]