On this page we’ve collected various research papers by the Evid Science team. We were born from a scientific project sponsored by the NIH, and we continue to actively engage with the scientific community. If you want to chat about our products, AI and healthcare, or frankly, any other scientific topic, shoot us a line!

Journal Articles:

Michelson M, Chow T, Martin NA, Ross M, Tee Qiao Ying A, Minton S. Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine. J Med Internet Res 2020; 22(8):e20007

Haynes RB, Del Fiol G, Michelson M, Iorio A. Context and Approach in Reporting Evaluations of Electronic Health Record–Based Implementation Projects. Annals of Internal Medicine 2020 172:11_Supplement, S73-S78

Michelson M and Reuter K. The Significant Cost of Systematic Reviews and Meta-Analyses: A Call for Greater Involvement of Machine Learning to Assess the Promise of Clinical Trials. Contemporary Clinical Trials Communications 2019; 16(1): 100443

Del Fiol G, Michelson M, Iorio A, Cotoi C, Haynes RB. A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study. J Med Internet Res 2018;20(6):e10281

Conference Abstracts:

Langford P, Chow T, Raynor M, Tuvey D, Knapton N, Casey M, Pashos C, Michelson M and Nolan K (2022) Lessons Learned by NICE Using an AI Platform to Replicate Literature Surveillance Tasks. IQWIG Information Retrieval Meeting (IRM). To appear.

Lionikaite V, Curry A and Brown AI (2021) Validation of Artificial Intelligence for Performing Systematic Literature Review Searching for Clinical Efficacy and Safety Data. ISPOR EU.

Islam K, Chow T, Wang S, Michelson M and Pashos C (2021) Machine Learning can Facilitate More Efficient Health Economic Literature Synthesis by Accurately Extracting Data from Published Abstracts. ISPOR EU.

Michelson M, Dogra R, and Goldberg N (2021) Artificial Intelligence Derived Literature Searches can Provide More Relevant Data, More Quickly than Traditional Reference Databases: A Case Study. 17th Annual Meeting of ISMPP.

Michelson M, Chow T, Mahida S, Manson S and Park J. (2020) Natural Language Processing to Understand the Landscape of Patient-Reported Outcomes in a Specific Disease Area. ISPOR EU.

Michelson M, Ross M & Minton, S. (2019) AI2: Leveraging Machine-Assistance to Replicate a Systematic Review. Value in Health. 22(2). S34. 10.1016/j.jval.2019.04.006.

Michelson M, Ross M & Minton, S. (2019) PNS261: How Does Machine-Learning Compare to an Incoming Medical Student in Extracting Outcomes Results from Abstracts? Value in Health. 22(2). S331. 10.1016/j.jval.2019.04.1616

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