MMiDaS-AE

• Type of Material: Article
• Authors: Eric W Lee, Emory University; Bryon C Wallace, Northeastern University; Karla Galaviz Arredondo, Emory University; Karla I Galaviz, Emory University; Joyce Ho, Emory University
• Language: English
• Date: 2020-02-04
• Publisher: Association for Computing Machinery
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020 Copyright held by the owner/author(s)
• Final Published Version (URL): http://dx.doi.org/10.1145/3368555.3384463
• Title of Journal or Parent Work: ACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning
• Volume: 2020
• Start Page: 139
• End Page: 150
• Grant/Funding Information: Byron C. Wallace was supported by the National Library of Medicine of the National Institutes of Health under award number 2R01LM012086. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.; Eric W. Lee and Joyce C. Ho were supported by the National Science Foundation award IIS-#1838200 and the National Institute of Health award 1K01LM012924–01. Karla I. Galaviz was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (P30DK111024).
• Keywords: Information systems → Clustering and classification; Multi-modal Stacked Autoencoder; Applied computing → Health informatics; Systematic Review; Missing Data Imputation
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a daunting process that entails multiple researchers screening thousands of articles for relevance. In this paper, we propose MMiDaS-AE, a Multi-modal Missing Data aware Stacked Autoencoder, for semi-automating screening for SRs. We use a multi-modal view that exploits three representations, of: 1) documents, 2) topics, and 3) citation networks. Documents that contain similar words will be nearby in the document embedding space. Models can also exploit the relationship between documents and the associated SR MeSH terms to capture article relevancy. Finally, related works will likely share the same citations, and thus closely related articles would, intuitively, be trained to be close to each other in the embedding space. However, using all three learned representations as features directly result in an unwieldy number of parameters. Thus, motivated by recent work on multi-modal auto-encoders, we adopt a multi-modal stacked autoencoder that can learn a shared representation encoding all three representations in a compressed space. However, in practice one or more of these modalities may be missing for an article (e.g., if we cannot recover citation information). Therefore, we propose to learn to impute the shared representation even when specific inputs are missing. We find this new model significantly improves performance on a dataset consisting of 15 SRs compared to existing approaches.