Reply: Improving care for patients with interstitial lung disease, using machine learning, requires transparency and reproducibility

Giulia C. Kennedy, Neil M. Barth, P. Sean Walsh, Jing Huang, Daniel G. Pankratz, Yoonha Choi, Grazyna M. Fedorowicz, Jessica D. Anderson, Ganesh Raghu, Fernando J. Martinez, Thomas V. Colby, David A. Lynch, Kevin K. Brown, Steve D. Groshong, Jeffrey L. Myers, Kevin R. Flaherty, Mark P. Steele

Research output: Contribution to journalLetter

Original languageEnglish (US)
Pages (from-to)1864-1865
Number of pages2
JournalAnnals of the American Thoracic Society
Volume14
Issue number12
DOIs
StatePublished - Dec 1 2017

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

Cite this

Kennedy, G. C., Barth, N. M., Sean Walsh, P., Huang, J., Pankratz, D. G., Choi, Y., Fedorowicz, G. M., Anderson, J. D., Raghu, G., Martinez, F. J., Colby, T. V., Lynch, D. A., Brown, K. K., Groshong, S. D., Myers, J. L., Flaherty, K. R., & Steele, M. P. (2017). Reply: Improving care for patients with interstitial lung disease, using machine learning, requires transparency and reproducibility. Annals of the American Thoracic Society, 14(12), 1864-1865. https://doi.org/10.1513/AnnalsATS.201708-654LE