Reporting of artificial intelligence prediction models

The Lancet

Volumn 393, Issue 10181, 2019, Pages 1577-1579

  Collins, Gary S ^a   Moons, Karel G M ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ASSISTIVE TECHNOLOGY; CLINICAL DECISION MAKING; INTERNATIONAL PROGNOSTIC INDEX; MACHINE LEARNING; MATHEMATICAL MODEL; METHODOLOGY; NOTE; PATIENT MONITORING; PREDICTION; PRIORITY JOURNAL; PROGNOSIS;

ARTIFICIAL INTELLIGENCE; PROGNOSIS;

EID: 85064322858     PISSN: 01406736     EISSN: 1474547X     Source Type: Journal    
DOI: 10.1016/S0140-6736(19)30037-6     Document Type: Note

References (13)

1. Wilson, PW, D'Agostino, RB, Levy, D, Belanger, AM, Silbershatz, H, Kannel, WB, Prediction of coronary heart disease using risk factor categories. Circulation 97 (1998), 1837–1847.
2. Hippisley-Cox, J, Coupland, C, Brindle, P, Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ, 357, 2017, j2099.
3. Malinchoc, M, Kamath, PS, Gordon, FD, Peine, CJ, Rank, J, ter Borg, PC, A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 31 (2000), 864–871.
4. Johnston, SC, Rothwell, PM, Nguyen-Huynh, MN, et al. Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet 369 (2007), 283–292.
5. van Gorp, MJ, Steyerberg, EW, van der Graaf, Y, Decision guidelines for prophylactic replacement of Bjork-Shiley convexo-concave heart valves: impact on clinical practice. Circulation 109 (2004), 2092–2096.
6. Damen, JAAG, Hooft, L, Schuit, E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, 2016, i2416.
7. Tangri, N, Kitsios, GD, Inker, LA, et al. Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med 158 (2013), 596–603.
8. Bouwmeester, W, Zuithoff, NP, Mallett, S, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med, 9, 2012, e1001221.
9. Collins, GS, de Groot, JA, Dutton, S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Meth, 14, 2014, 40.
10. Collins, GS, Reitsma, JB, Altman, DG, Moons, KGM, Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis: the TRIPOD statement. Ann Intern Med 162 (2015), 55–63.
11. Moons, KGM, Altman, DG, Reitsma, JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162 (2015), W1–W73.
12. Chen, JH, Asch, SM, Machine Learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med 376 (2017), 2507–2509.
13. Ratner, M, FDA backs clinician-free AI imaging diagnostic tools. Nat Biotechnol 36 (2018), 673–674.