who: Byron Wallace (Northeastern)
when: Sept 26 (Thursday) 11:45a – 1:15p
where: Computer Science Building Rm 150
food: Athena’s Pizza
What does the evidence say?
Models to help make sense of the biomedical literature
Abstract:
How do we know if a particular medical intervention actually works better than the alternatives for a given condition and outcome? Ideally one would consult all available evidence from relevant trials that have been conducted to answer this question. Unfortunately, such results are primarily disseminated in natural language articles that describe the conduct and results of clinical trials. This imposes substantial burden on physicians and other domain experts trying to make sense of the evidence. In this talk I will discuss work on designing tasks, corpora, and models that aim to realize natural language technologies that can extract key attributes of clinical trials from articles describing them, and infer the reported findings regarding these. The hope is to use such methods to help domain experts (such as physicians) access and make sense of unstructured biomedical evidence.
More specifically, I will discuss models to automatically extract trial population characteristics (e.g., conditions), interventions/comparators (treatments), and outcomes studied in a given clinical trial; together these “PICO” elements compose well-formed clinical questions. I will then present ongoing work on corpora and models for inferring the comparative effectiveness of a given treatment, as compared to a specified comparator, and with respect to a particular outcome of interest. If successfully realized (a big if), such models would effectively facilitate real-time clinical question answering over reports of clinical trials, in turn enabling evidence-based care.
Bio:
Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He holds a PhD in Computer Science from Tufts University, where he was advised by Carla Brodley. He has previously held faculty positions at the University of Texas at Austin and at Brown University. His research is in machine learning and natural language processing, with an emphasis on their application in health informatics.
Wallace’s work has been supported by grants from the National Science Foundation (including a CAREER award), the National Institutes for Health, and the Army Research Office. He won the Tufts University 2012 Outstanding Graduate Researcher award, and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics, and co-authored the 2017 Distinguished Clinical Research Informatics Paper Award winner at the American Medical Informatics Association Joint Summits on Translational Sciences. He also received the 2018 Early Career Award from the Society for Research Synthesis.