Benjamin Fung

Associate Academic Member

Associate Professor, McGill University, School of Information Studies

Research Topics

Data Mining

Biography

Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, as well as a full professor at the School of Information Studies and associate member of the School of Computer Science, McGill University.

Fung serves as an associate editor of IEEE Transactions of Knowledge and Data Engineering and Sustainable Cities and Society. He received his PhD in computing science from Simon Fraser University in 2007.

Dr. Fung has over 150 refereed publications to his credit and and more than 14,000 citations (h-index 57) spanning the fields of data mining, machine learning, privacy, cybersecurity and building engineering. His findings in the fields of data mining for crime investigations and authorship analysis have been reported by the media worldwide.

Publications

Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records

Kejing Yin

William K. Cheung

Benjamin Fung

Jonathan Poon

Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health record… (see more)s (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.

2020-11-12

ArXiv (preprint)

doi.org

arxiv.org

Trends and Applications in Knowledge Discovery and Data Mining

Lida Rashidi

Benjamin Fung

Can Wang

2018-01-01

Lecture Notes in Computer Science (published)

doi.org

Trends and Applications in Knowledge Discovery and Data Mining