[1]
|
Lee CH, Yoon HJ. Medical big data: promise and challenges[J]. Kidney Res Clin Pract, 2017, 36:3-11. doi: 10.23876/j.krcp.2017.36.1.3 |
[2]
|
Price WN, Cohen IG. Privacy in the age of medical big data[J]. Nat Med, 2019, 25:37-43. doi: 10.1038/s41591-018-0272-7 |
[3]
|
Chan MK, Cooper JD, Bahn S. Commercialisation of biomarker tests for mental illnesses: advances and obstacles[J]. Trends Biotechnol, 2015, 33:712-723. doi: 10.1016/j.tibtech.2015.09.010 |
[4]
|
Ranstam J, Buyse M, George SL, et al. Fraud in medical research: an international survey of biostatisticians[J]. Controll Clini Trials, 2000, 21:415-427. doi: 10.1016/S0197-2456(00)00069-6 |
[5]
|
Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[J]. arXiv preprint, 2014, 1412.6572. https://arxiv.org/abs/1412.6572 |
[6]
|
Erickson BJ, Korfiatis P, Akkus Z, et al. Machine learning for medical imaging[J]. Radiographics, 2017, 37:505-515. doi: 10.1148/rg.2017160130 |
[7]
|
Dallachiesa M, Ebaid A, Eldawy A, et al. NADEEF: a commodity data cleaning system[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, ACM, 2013: 541-552. https://www.researchgate.net/publication/266653693_NADEEF_a_commodity_data_cleaning_system?_sg=DCFqDNvnXTKkHNIIbpCv_Ikp_w7YpyKUxtygQXlLca_k3v6yNoNnvNZ3sfyEzKJXiiYqNOa_HI9MUWUfM7xPkw |
[8]
|
Rahm E, Do HH. Data cleaning: Problems and current approaches[J]. IEEE Data Eng, Bull, 2000, 23:3-13. http://ci.nii.ac.jp/naid/10018221721 |
[9]
|
Pauleen DJ, Rooney D, Intezari A. Big data, little wisdom: trouble brewing? Ethical implications for the information systems discipline[J]. Soc Epistemol, 2017, 31:400-416. doi: 10.1080/02691728.2016.1249436 |
[10]
|
McCaul ME, Wand GS. Detecting deception in our research participants: are your participants who you think they are?[J]. Alcoholism Clin Exp Res, 2018, 42:230-237. doi: 10.1111/acer.13556 |
[11]
|
Rohrer JM. Thinking clearly about correlations and causation: Graphical causal models for observational data[J]. Advances in Methods and Practices in Psychological Science (AMPPS), 2018, 1:27-42. doi: 10.1177/2515245917745629 |
[12]
|
Simon HA. Spurious correlation: A causal interpretation[J]. J Am Stat Assoc, 1954, 49:467-479. doi: 10.1007%2F978-94-010-9521-1_7 |
[13]
|
Wilson N, Mason K, Tobias M, et al. Interpreting "Google Flu Trends" data for pandemic H1N1 influenza: the New Zealand experience[J]. Euro Surveill, 2009, 14:19386. http://europepmc.org/abstract/MED/19941777 |
[14]
|
Lazer D, Kennedy R, King G, et al. Big data. The parable of Google Flu: traps in big data analysis[J]. Science, 2014, 343:1203-1205. doi: 10.1126/science.1248506 |
[15]
|
Butler D. When Google got flu wrong[J]. Nature, 2013, 494:155. doi: 10.1038/494155a |
[16]
|
Taleb NN. The black swan: the impact of the highly improbable[M]. New York:Random house, 2007. |
[17]
|
Siuly S, Zhang Y. Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis[J]. Data Science and Engineering (DSE), 2016, 1:54-64. doi: 10.1007/s41019-016-0011-3 |
[18]
|
Batrouni M, Bertaux A, Nicolle C. Scenario analysis, from BigData to black swan[J]. Comput Sci Rev, 2018, 28:131-139. doi: 10.1016/j.cosrev.2018.02.001 |
[19]
|
Doornik JA, Hendry DF. Statistical model selection with "Big Data "[J]. Cogent Economics & Finance, 2015, 3:1045216. doi: 10.1080/23322039.2015.1045216 |
[20]
|
Elsayed G, Shankar S, Cheung B, et al. Adversarial examples that fool both computer vision and time-limited humans[C]//Advances in Neural Information Processing Systems, 2018: 3910-3920. doi: 10.5555/3327144.3327306 |
[21]
|
Feinman R, Curtin RR, Shintre S, et al. Detecting Adversarial Samples from Artifacts[J]. arXiv preprint, 2017, 1703.00410. https://www.researchgate.net/publication/314153095_Detecting_Adversarial_Samples_from_Artifacts |
[22]
|
Lipton ZC. The mythos of model interpretability[J]. Queue, 2018, 16:31-57. http://portal.acm.org/citation.cfm?id=3241340 |
[23]
|
Poursabzi-Sangdeh F, Goldstein DG, Hofman JM, et al. Manipulating and Measuring Model Interpretability[J]. arXiv preprint, 2018, 1802.07810. https://www.researchgate.net/publication/323355908_Manipulating_and_Measuring_Model_Interpretability |