Real-life clinical data mining: generating hypotheses for evidence-based medicine

Affiliations

Editorial

Jean-Emmanuel Bibault. Ann Transl Med. 2020 Feb.

. 2020 Feb;8(4):69.

doi: 10.21037/atm.2019.10.99.

¹ Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, Stanford, CA, USA.

No abstract available

Conflict of interest statement

Conflicts of Interest: The author has no conflicts of interest to declare.

In-depth mining of clinical data: the construction of clinical prediction model with R.
Zhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, Chen YS, Wang SJ, Hu J, Zhang HN, Huang P, Zhao GZ, Chen XX, Li B, Zhang TS. Zhou ZR, et al. Ann Transl Med. 2019 Dec;7(23):796. doi: 10.21037/atm.2019.08.63. Ann Transl Med. 2019. PMID: 32042812 Free PMC article.

1. Chen C, He M, Zhu Y, et al. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev 2015;34:313-8. 10.1007/s10555-015-9555-3 - DOI - PubMed
1. Geifman N, Butte AJ. Do cancer clinical trial populations truly represent cancer patients? A comparison of open clinical trials to the cancer genome atlas. Pac Symp Biocomput 2016;21:309-20. - PMC - PubMed
1. Gligorijević V, Malod-Dognin N, Pržulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016;16:741-58. 10.1002/pmic.201500396 - DOI - PubMed
1. Sagiroglu S, Sinanc D. Big data: A review. In: Collaboration Technologies and Systems (CTS), 2013 International Conference on. IEEE, 2013:42-7.
1. Andreu-Perez J, Poon CC, Merrifield RD, et al. Big data for health. IEEE J Biomed Health Inform 2015;19:1193-208. 10.1109/JBHI.2015.2450362 - DOI - PubMed