Predicting drug approvals: The Novartis data science and artificial intelligence challenge
Name
1-s2.0-S2666389921001550-main.pdf
Description
Published version
Size
931.96 KB
Format
Adobe PDF
Checksum (MD5)
defba8229926f3497f66849872a9e30f
Author(s)
Siah, Kien Wei
Kelley, Nicholas W
Ballerstedt, Steffen
Holzhauer, Björn
Lyu, Tianmeng
Mettler, David
Sun, Sophie
Wandel, Simon
Zhong, Yang
Zhou, Bin
Date Issued
August 2021
Journal
Patterns
Publisher
Elsevier BV
Citation
Siah, Kien Wei, Kelley, Nicholas W, Ballerstedt, Steffen, Holzhauer, Björn, Lyu, Tianmeng et al. 2021. "Predicting drug approvals: The Novartis data science and artificial intelligence challenge." Patterns, 2 (8).
Version
Final published version
Abstract
We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model-areas under the curve of 0.88 and 0.84 versus 0.78, respectively-through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.
MIT Department
Sloan School of Management. Laboratory for Financial Engineering
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.patter.2021.100312
