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!
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
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.
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, 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
Ross M, Michelson M, Tee Qiao Ying A, Ashish N, Minton N (2019) PNS265: Automated Discovery of Comparative Effectiveness Hypotheses from Medical Literature. Value in Health. 22(2). S331. 10.1016/j.jval.2019.04.1620