My research is in machine learning/data mining and natural language processing, with an emphasis on applications in health informatics.
For example, one of my core ongoing research aims concerns optimizing the processes of evidence-based medicine using novel natural language processing and machine learning methods. The aim is to reduce the (human) workload involved in conducting systematic reviews, so that we can realize the aim of evidence-based care in an era of information overload.
More broadly, I am interested in core machine learning/natural language processing issues: e.g., structured and unstructured classiﬁcation techniques; semi-supervised learning methods; learning from imbalanced data; and learning from alternative forms of supervision. Finally, I have recently been involved with projects that involve statistical models for novel problems in NLP, including narrative and sociolinguistic structures.
01/27/2016 Speaking at MLDAS
I'll be speaking at the Machine Learning and Data Analytics Symposium (MLDAS) in Qatar this March.
11/01/2015 ICHI 2015 data analytics challenge
09/17/2015 NIH/NLM R01 grant
The National Institutes of Health (NIH) has selected our grant, "Semi-Automating Data Extraction for Systematic Reviews", for funding!
Byron C Wallace, Christopher H Schmid, Joseph Lau and Thomas A Trikalinos. Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data BMC Research Methods; 2009.
Byron C. Wallace, Issa J. Dahabreh, Kelly H. Moran, Carla E. Brodley and Thomas A. Trikalinos. Active Literature Discovery for Scoping Evidence Reviews: How Many Needles are There? KDD Workshop on Data Mining for Healthcare (KDD-DMH); 2013.
Iain J. Marshall, Joël Kuiper and Byron C. Wallace. Automating Risk of Bias Assessment for Clinical Trials ACM Conference on Bioinformatics, Computational Biology, and Health Informatics; 2014.