Making Sense of the Biomedical Literature via Machine Learning and Natural Language Processing
Making Sense of the Biomedical Literature via Machine Learning and Natural Language Processing


"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 methods that look to optimize the practice of EBM and thus mitigate the burden on those trying to make sense of the clinical evidence base. Specifically, I will describe methods 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 automating the important step of assessing clinical trials for risks of bias using convolutional neural networks. These tasks pose challenging problems from a machine learning vantage point, motivating novel approaches. For example, I will describe a new method for interpretable neural text classification which was motivated by our work on automating bias assessment for articles describing clinical trials. 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."


Byron Wallace is an assistant professor in the College of Computer and Information Science 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 primary research is in machine learning and natural language processing methods, with an emphasis on their application in health informatics.

Wallace's work has been supported by grants from the National Science Foundation (NSF; including a CAREER award in 2018), the National Institutes for Health (NIH), 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 co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics, and his recent work with colleagues received the 2017 Distinguished Clinical Research Informatics Paper Award at the American Medical Informatics Association Joint Summits on Translational Sciences.

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