-
1
-
-
52949100612
-
Validation, updating and impact of clinical prediction rules: A review
-
Toll DB, Janssen KJ, Vergouwe Y et al. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol 2008; 61: 1085-1094.
-
(2008)
J Clin Epidemiol
, vol.61
, pp. 1085-1094
-
-
Toll, D.B.1
Janssen, K.J.2
Vergouwe, Y.3
-
2
-
-
75349094042
-
Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines
-
Waljee AK, Joyce JC, Wang SJ et al. Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. Clin Gastroenterol Hepatol 2010; 8: 143-150.
-
(2010)
Clin Gastroenterol Hepatol
, vol.8
, pp. 143-150
-
-
Waljee, A.K.1
Joyce, J.C.2
Wang, S.J.3
-
3
-
-
84887246574
-
Machine learning algorithms outperform conventional regression models in identifying risk factors for hepatocellular carcinoma in patients with cirrhosis
-
Singal AG, Mukherjee A, Higgins PD et al. Machine learning algorithms outperform conventional regression models in identifying risk factors for hepatocellular carcinoma in patients with cirrhosis. Am J Gastroenterol 2013; 108: 1723-1730.
-
(2013)
Am J Gastroenterol
, vol.108
, pp. 1723-1730
-
-
Singal, A.G.1
Mukherjee, A.2
Higgins, P.D.3
-
4
-
-
84866148504
-
Failure rates in the hepatocellular carcinoma surveillance process
-
Singal AG, Yopp AC, Gupta S et al. Failure rates in the hepatocellular carcinoma surveillance process. Cancer Prev Res (Phila) 2012; 5: 1124-1130.
-
(2012)
Cancer Prev Res (Phila)
, vol.5
, pp. 1124-1130
-
-
Singal, A.G.1
Yopp, A.C.2
Gupta, S.3
-
5
-
-
84884353768
-
An automated model using electronic medical record data to identify patients with cirrhosis at high risk for readmission
-
Singal AG, Rahimi RS, Clark C et al. An automated model using electronic medical record data to identify patients with cirrhosis at high risk for readmission. Clinical Gastroenterol and Hepatol 2013; 11: 1335-1341.
-
(2013)
Clinical Gastroenterol and Hepatol
, vol.11
, pp. 1335-1341
-
-
Singal, A.G.1
Rahimi, R.S.2
Clark, C.3
-
6
-
-
67650022801
-
Prognosis and prognostic research: What, why and how?
-
Moons KG, Royston P, Vergouwe Y et al. Prognosis and prognostic research: what, why, and how? BMJ 2009; 338: b375.
-
(2009)
BMJ
, vol.338
-
-
Moons, K.G.1
Royston, P.2
Vergouwe, Y.3
-
7
-
-
76249132458
-
Ten steps towards improving prognosis research
-
Hemingway H, Riley RD, Altman DG. Ten steps towards improving prognosis research. BMJ 2009; 339: b4184.
-
(2009)
BMJ
, vol.339
-
-
Hemingway, H.1
Riley, R.D.2
Altman, D.G.3
-
8
-
-
77953215733
-
Machine learning in medicine: A primer for physicians
-
Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol 2010; 105: 1224-1226.
-
(2010)
Am J Gastroenterol
, vol.105
, pp. 1224-1226
-
-
Waljee, A.K.1
Higgins, P.D.2
-
9
-
-
78650077217
-
Real-time tool to display the predicted disease course and treatment response for children with Crohn's disease
-
Siegel CA, Siegel LS, Hyams JS et al. Real-time tool to display the predicted disease course and treatment response for children with Crohn's disease. Inflamm Bowel Dis 2011; 17: 30-38.
-
(2011)
Inflamm Bowel Dis
, vol.17
, pp. 30-38
-
-
Siegel, C.A.1
Siegel, L.S.2
Hyams, J.S.3
-
10
-
-
84863304598
-
-
Core Team R Foundation for Statistical Computing, Vienna, Austria ISBN 3-900051-07-0
-
Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013, ISBN 3-900051-07-0, http://www.R-project.org/.
-
(2013)
R: A Language and Environment for Statistical Computing
-
-
-
11
-
-
0035478854
-
Random forests
-
DOI 10.1023/A:1010933404324
-
Breiman L. Random forests. Machine Learning 2001; 45: 5-32. (Pubitemid 32933532)
-
(2001)
Machine Learning
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
12
-
-
0345040873
-
Classification and regression by random Forest
-
Liaw A, Wiener M. Classification and regression by random Forest. R News 2002; 2: 18-22.
-
(2002)
R News
, vol.2
, pp. 18-22
-
-
Liaw, A.1
Wiener, M.2
-
13
-
-
67650045441
-
Prognosis and prognostic research: Developing a prognostic model
-
Royston P, Moons KG, Altman DG et al. Prognosis and prognostic research: developing a prognostic model. BMJ 2009; 338: b604.
-
(2009)
BMJ
, vol.338
-
-
Royston, P.1
Moons, K.G.2
Altman, D.G.3
-
14
-
-
0024503860
-
Modeling and variable selection in epidemiologic analysis
-
Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989; 79: 340-349. (Pubitemid 19077878)
-
(1989)
American Journal of Public Health
, vol.79
, Issue.3
, pp. 340-349
-
-
Greenland, S.1
-
15
-
-
84866615945
-
Missing data in clinical studies: Issues and methods
-
Ibrahim JG, Chu H, Chen MH. Missing data in clinical studies: issues and methods. J Clin Oncol 2012; 30: 3297-3303.
-
(2012)
J Clin Oncol
, vol.30
, pp. 3297-3303
-
-
Ibrahim, J.G.1
Chu, H.2
Chen, M.H.3
-
16
-
-
84862748393
-
Do the methods used to analyse missing data really matter? An examination of data from an observational study of Intermediate Care patients
-
Kaambwa B, Bryan S, Billingham L. Do the methods used to analyse missing data really matter? An examination of data from an observational study of Intermediate Care patients. BMC Res Notes 2012; 5: 330.
-
(2012)
BMC Res Notes
, vol.5
, pp. 330
-
-
Kaambwa, B.1
Bryan, S.2
Billingham, L.3
-
17
-
-
84884547259
-
Comparison of modern imputation methods for missing laboratory data inmedicine
-
pii doi:10.1136/bmjopen-2013-002847
-
Waljee A, Mukherjee A, Singal A et al. Comparison of modern imputation methods for missing laboratory data inmedicine. BMJOpen 2013; 3: pii: e002847; doi:10.1136/bmjopen-2013-002847.
-
(2013)
BMJOpen
, vol.3
-
-
Waljee, A.1
Mukherjee, A.2
Singal, A.3
-
18
-
-
67650082402
-
Prognosis and prognostic research: Validating a prognostic model
-
Altman DG, Vergouwe Y, Royston P et al. Prognosis and prognostic research: validating a prognostic model. BMJ 2009; 338: b605.
-
(2009)
BMJ
, vol.338
-
-
Altman, D.G.1
Vergouwe, Y.2
Royston, P.3
-
19
-
-
0034906866
-
Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis
-
DOI 10.1016/S0895-4356(01)00341-9, PII S0895435601003419
-
Steyerberg EW, Harrell FE Jr, Borsboom GJ et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54: 774-781. (Pubitemid 32722180)
-
(2001)
Journal of Clinical Epidemiology
, vol.54
, Issue.8
, pp. 774-781
-
-
Steyerberg, E.W.1
Harrell, Jr.F.E.2
Borsboom, G.J.J.M.3
Eijkemans, M.J.C.4
Vergouwe, Y.5
Habbema, J.D.F.6
-
20
-
-
73849094087
-
Assessing the performance of prediction models: A framework for traditional and novel measures
-
Steyerberg EW, Vickers AJ, Cook NR et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21: 128-138.
-
(2010)
Epidemiology
, vol.21
, pp. 128-138
-
-
Steyerberg, E.W.1
Vickers, A.J.2
Cook, N.R.3
-
21
-
-
54349088695
-
The performance of risk prediction models
-
Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J 2008; 50: 457-479.
-
(2008)
Biom J
, vol.50
, pp. 457-479
-
-
Gerds, T.A.1
Cai, T.2
Schumacher, M.3
-
22
-
-
70450231574
-
Predictive model assessment for count data
-
Czado C, Gneiting T, Held L. Predictive model assessment for count data. Biometrics 2009; 65: 1254-1261.
-
(2009)
Biometrics
, vol.65
, pp. 1254-1261
-
-
Czado, C.1
Gneiting, T.2
Held, L.3
-
23
-
-
0031006441
-
A comparison of goodness-of-fit tests for the logistic regression model
-
DOI 10.1002/(SICI)1097-0258(19970515) 16:9<965::AID-SIM509>3.0. CO;2-O
-
Hosmer DW, Hosmer T, Le Cessie S et al. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997; 16: 965-980. (Pubitemid 27198985)
-
(1997)
Statistics in Medicine
, vol.16
, Issue.9
, pp. 965-980
-
-
Hosmer, D.W.1
Hosmer, T.2
Le Cessie, S.3
Lemeshow, S.4
-
24
-
-
15044357936
-
Survival model predictive accuracy and ROC curves
-
DOI 10.1111/j.0006-341X.2005.030814.x
-
Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61: 92-105. (Pubitemid 40380968)
-
(2005)
Biometrics
, vol.61
, Issue.1
, pp. 92-105
-
-
Heagerty, P.J.1
Zheng, Y.2
-
25
-
-
33847109797
-
Use and misuse of the receiver operating characteristic curve in risk prediction
-
DOI 10.1161/CIRCULATIONAHA.106.672402, PII 0000301720070220000018
-
Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115: 928-935. (Pubitemid 46294733)
-
(2007)
Circulation
, vol.115
, Issue.7
, pp. 928-935
-
-
Cook, N.R.1
-
26
-
-
84855232027
-
Novel metrics for evaluating improvement in discrimination: Net reclassification and integrated discrimination improvement for normal variables and nested models
-
Pencina MJ, D'Agostino RB, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med 2012; 31: 101-113.
-
(2012)
Stat Med
, vol.31
, pp. 101-113
-
-
Pencina, M.J.1
D'Agostino, R.B.2
Demler, O.V.3
-
27
-
-
67650089602
-
Prognosis and prognostic research: Application and impact of prognostic models in clinical practice
-
Moons KG, Altman DG, Vergouwe Y et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009; 338: b606.
-
(2009)
BMJ
, vol.338
-
-
Moons, K.G.1
Altman, D.G.2
Vergouwe, Y.3
-
28
-
-
33644855006
-
Translating clinical research into clinical practice: Impact of using prediction rules to make decisions
-
Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006; 144: 201-209. (Pubitemid 46780691)
-
(2006)
Annals of Internal Medicine
, vol.144
, Issue.3
, pp. 201-209
-
-
Reilly, B.M.1
Evans, A.T.2
-
29
-
-
83155184568
-
Affordable Care Act: Predictive modeling challenges and opportunities for case management
-
quiz 22-23
-
Meek JA. Affordable Care Act: predictive modeling challenges and opportunities for case management. Prof Case Manag 2012; 17: 15-21; quiz 22-23.
-
(2012)
Prof Case Manag
, vol.17
, pp. 15-21
-
-
Meek, J.A.1
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