Fully Automated Scholarly Search for Biomedical Systematic Literature Reviews

#Biomedical #Search #SystematicLiteratureReviews #WIE

We are very pleased to announce the invited talk entitled "Fully Automated Scholarly Search for Biomedical Systematic Literature Reviews" presented by Dr. Faezeh Ensan, Assistant Professor at TMU.    
The event will take place virtually via Zoom on March 19, 2024, at 12:00pm.  
Please register here: Zoom Registration Link.

  Date and Time




  • Date: 19 Mar 2024
  • Time: 04:04 PM to 04:06 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
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  • Starts 14 March 2024 12:00 AM
  • Ends 19 March 2024 12:00 AM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
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Faezeh Ensan of Toronto Metropolitan University


Fully Automated Scholarly Search for Biomedical Systematic Literature Reviews

Biomedical Systematic Literature Reviews (SLRs) play a fundamental role in evidence-informed healthcare and can serve as actionable insights for researchers and policy-making organizations in the field. In this presentation, we focus on the phase of ‘study search’ in conducting SLRs, i.e., the process of organising a comprehensive search via biomedical databases, such PubMed, in order to obtain all the relevant articles on a certain topic of interest. We introduce FASS-BSLR, a dataset and a benchmark suit to facilitate developing and evaluating fully automated techniques for study search. We also provide and analyze a set of basic methods along with a number of generative models, and report the experiment’s results over the introduced dataset. We introduce a simple but effective model based on the recent transformer-based generative model, ChatGPT, for generating Boolean queries over PubMed. Through different experiments, we illustrate that this model is more effective than basic search models, than keyword search over PubMed, and than existing methods for crafting Boolean queries using ChatGPT. We show that the introduced model is even more effective than manual queries in terms of Precision, Recall, NDCG, and MAP in positions 10, and 100, but falls short of the recall that manual queries achieve at position 1000. We also report the retrieval performance of different models when a number of relevant articled have been provided as seed documents. We demonstrate that, when three documents are used as seed articles, the introduced model outperforms manual queries in all metrics except Recall@1000, on which its performance is comparable with the performance attained by manualqueries.


Faezeh Ensan received the PhD degrees in Computer Science from the University of New Brunswick, Fredericton, Canada, in 2011. From 2011 to 2019, she worked as Research Assistant at the University of British Columbia, and as data scientist in the Semantic Technologies Laboratory, Athabasca University, Canada. Since 2019, she has been an Assistant Professor with the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University, Toronto, Canada. She has published in several venues such as Information Systems Journal, Knowledge and Information Systems Journal, Information Processing and Management Journal, AAAI, CIKM, WSDM and IPM. She was a guest editor for the special issue in Elsevier’s Information Systems journal and a guest Editor in Elsevier’s Journal of Biomedical Informatics. Also she was the Program co-Chair of the Canadian Semantic Web in 2009 and edited a subsequent book published by Springer (Canadian semantic web:Technologies and applications.)