[Colloq] REMINDER: Automating Clinical Evidence Synthesis via Machine Learning and Natural Language Processing | Byron Wallace, University of Texas, Austin | December 14, 2015 10:30-11:30am 366WVH (TODAY)

[Walker, Lashauna]

Title: Automating Clinical Evidence Synthesis via Machine Learning and Natural Language Processing
Speaker: Byron Wallace
Date: December 14, 2015
Time: 10:30am-11:30am
Location: 366 WVH

Title: Automating Clinical Evidence Synthesis via Machine Learning and Natural Language Processing

Abstract:
Evidence-based medicine (EBM) looks to inform patient care with the totality of the available evidence. Systematic reviews, which statistically synthesize the entirety of the biomedical literature pertaining to a specific clinical question, are the cornerstone of EBM. These reviews are critical to modern healthcare, informing everything from national health policy to bedside decision-making. But conducting systematic reviews is extremely laborious and hence expensive. Producing a single review requires thousands of expert hours. Moreover, the exponential expansion of the biomedical literature base has imposed an unprecedented burden on reviewers, thus multiplying costs. Researchers can no longer keep up with the primary literature, and this hinders the practice of evidence-based care.

I will discuss recent work on machine learning and natural language processing approaches that look to optimize the practice of EBM and thus mitigate the burden on reviewers. Specifically, I will describe a method for automatic identification of clinically salient information in full text articles (descriptions of the population, interventions and outcomes studied; collectively referred to as PICO elements). And I will describe work on semi-automating the important step of assessing clinical trials for risks of bias. These tasks pose challenging problems from a machine learning vantage point, motivating the development of novel approaches. For example, I will describe (1) a new framework for "distantly supervised" learning that we introduce for PICO identification, and, (2) a hierarchical multi-task learning approach motivated by our work on automating risk of bias assessments. I will present evaluations of these methods in the context of EBM. Finally, I will highlight promising directions moving forward toward automating evidence synthesis, including hybrid crowd-sourced/machine learning systems.

Short bio:
Byron Wallace is an assistant professor in the School of Information and the Department of Computer Science (by courtesy) at the University of Texas at Austin. He holds a PhD in Computer Science from Tufts University, where he was advised by Carla Brodley. Prior to joining UT, he was research faculty at Brown University, where he was part of the Center for Evidence-Based Medicine and also affiliated with the Brown Laboratory for Linguistic Information Processing. His primary research is in machine learning and natural language processing methods, with an emphasis on their application in health informations (and especially evidence-based medicine). Wallace's work is supported by grants from the National Institutes for Health (NIH), the National Science Foundation (NSF), and the Army Research Office (ARO). 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 recently co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics


Thank You.

LaShauna Walker
Events and Administrative Specialist
College of Computer and Information Science
Northeastern University
617-373-2763
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