-
1
-
-
78751507447
-
Hospital epidemiology and infection control in acute-care settings
-
Sydnor ER, Perl TM. Hospital epidemiology and infection control in acute-care settings. Clin Microbiol Rev 2011;24:141-173.
-
(2011)
Clin Microbiol Rev
, vol.24
, pp. 141-173
-
-
Sydnor, E.R.1
Perl, T.M.2
-
3
-
-
85040572144
-
Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology
-
Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis 2018;66:149-153.
-
(2018)
Clin Infect Dis
, vol.66
, pp. 149-153
-
-
Wiens, J.1
Shenoy, E.S.2
-
5
-
-
84905990877
-
Big data in health care: Using analytics to identify and manage high-risk and highcost patients
-
Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and highcost patients. Health Aff 2014;33:1123-1131.
-
(2014)
Health Aff
, vol.33
, pp. 1123-1131
-
-
Bates, D.W.1
Saria, S.2
Ohno-Machado, L.3
Shah, A.4
Escobar, G.5
-
6
-
-
0000793139
-
Cramming more components onto integrated circuits
-
Moore GE. Cramming more components onto integrated circuits. Electronics 1965;38:114-117.
-
(1965)
Electronics
, vol.38
, pp. 114-117
-
-
Moore, G.E.1
-
7
-
-
84937801713
-
Machine learning: Trends, perspectives, and prospects
-
Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015;349:255-260.
-
(2015)
Science
, vol.349
, pp. 255-260
-
-
Jordan, M.I.1
Mitchell, T.M.2
-
8
-
-
85040096804
-
Digital epidemiology: What is it, and where is it going?
-
Salathé M. Digital epidemiology: What is it, and where is it going? Life Sci Soc Policy 2018;14:1.
-
(2018)
Life Sci Soc Policy
, vol.14
, pp. 1
-
-
Salathé, M.1
-
9
-
-
85015899308
-
Digital pharmacovigilance and disease surveillance: Combining traditional and big-data systems for better public health
-
Salathé M. Digital pharmacovigilance and disease surveillance: combining traditional and big-data systems for better public health. J Infect Dis 2016;214:S399-S403.
-
(2016)
J Infect Dis
, vol.214
, pp. S399-S403
-
-
Salathé, M.1
-
10
-
-
84880913661
-
Influenza A (H7N9) and the importance of digital epidemiology
-
Salathé M, Freifeld CC, Mekaru SR, Tomasulo AF, Brownstein JS. Influenza A (H7N9) and the importance of digital epidemiology. N Engl J Med 2013;369:401-404.
-
(2013)
N Engl J Med
, vol.369
, pp. 401-404
-
-
Salathé, M.1
Freifeld, C.C.2
Mekaru, S.R.3
Tomasulo, A.F.4
Brownstein, J.S.5
-
11
-
-
85021767773
-
Automated surveillance of healthcare-associated infections: State of the art
-
Sips ME, Bonten MJM, van Mourik MSM. Automated surveillance of healthcare-associated infections: state of the art. Curr Opin Infect Dis 2017;30:425-431.
-
(2017)
Curr Opin Infect Dis
, vol.30
, pp. 425-431
-
-
Sips, M.E.1
Bonten, M.J.M.2
Van Mourik, M.S.M.3
-
12
-
-
85053111688
-
Big data's role in precision public health
-
Dolley S. Big data's role in precision public health. Front Public Health 2018;6:68.
-
(2018)
Front Public Health
, vol.6
, pp. 68
-
-
Dolley, S.1
-
13
-
-
85102429043
-
Challenges and opportunities of big data in health care: A systematic review
-
Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform 2016;4:e38.
-
(2016)
JMIR Med Inform
, vol.4
, pp. e38
-
-
Kruse, C.S.1
Goswamy, R.2
Raval, Y.3
Marawi, S.4
-
14
-
-
84906222774
-
Big data and large sample size: A cautionary note on the potential for bias
-
Kaplan RM, Chambers DA, Glasgow RE. Big data and large sample size: a cautionary note on the potential for bias. Clin Transl Sci 2014;7:342-346.
-
(2014)
Clin Transl Sci
, vol.7
, pp. 342-346
-
-
Kaplan, R.M.1
Chambers, D.A.2
Glasgow, R.E.3
-
15
-
-
84920847860
-
Comparative effectiveness research and big data: Balancing potential with legal and ethical considerations
-
Gray EA, Thorpe JH. Comparative effectiveness research and big data: balancing potential with legal and ethical considerations. J Comp Eff Res 2015;4:61-74.
-
(2015)
J Comp Eff Res
, vol.4
, pp. 61-74
-
-
Gray, E.A.1
Thorpe, J.H.2
-
16
-
-
0001201756
-
Some studies in machine learning using the game of checkers
-
Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Devel 1959;3:210-229.
-
(1959)
IBM J Res Devel
, vol.3
, pp. 210-229
-
-
Samuel, A.L.1
-
17
-
-
0000209388
-
The regression analysis of binary sequences
-
Cox D. The regression analysis of binary sequences. J Roy Stat Soc 1958:215-242.
-
(1958)
J Roy Stat Soc
, pp. 215-242
-
-
Cox, D.1
-
19
-
-
0035478854
-
Random forests
-
Breiman L. Random forests. Machine Learn 2001;45:5-32.
-
(2001)
Machine Learn
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
20
-
-
85044594960
-
A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers
-
Oh J, Makar M, Fusco C, et al. A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Infect Control Hosp Epidemiol 2018;39:425-433.
-
(2018)
Infect Control Hosp Epidemiol
, vol.39
, pp. 425-433
-
-
Oh, J.1
Makar, M.2
Fusco, C.3
-
21
-
-
85032453950
-
Calibration drift in regression and machine learning models for acute kidney injury
-
Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc 2017;24:1052-1061.
-
(2017)
J Am Med Inform Assoc
, vol.24
, pp. 1052-1061
-
-
Davis, S.E.1
Lasko, T.A.2
Chen, G.3
Siew, E.D.4
Matheny, M.E.5
-
22
-
-
85029857162
-
Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system
-
Escobar GJ, Baker JM, Kipnis P, et al. Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system. Infect Control Hosp Epidemiol 2017;38:1196-1203.
-
(2017)
Infect Control Hosp Epidemiol
, vol.38
, pp. 1196-1203
-
-
Escobar, G.J.1
Baker, J.M.2
Kipnis, P.3
-
23
-
-
85056189344
-
Leveraging clinical timeseries data for prediction: A cautionary tale
-
Sherman E, Gurm H, Balis U, Owens S, Wiens J. Leveraging clinical timeseries data for prediction: a cautionary tale. AMIA Annu Symp Proc 2017;2017:1571-1580.
-
(2017)
AMIA Annu Symp Proc
, vol.2017
, pp. 1571-1580
-
-
Sherman, E.1
Gurm, H.2
Balis, U.3
Owens, S.4
Wiens, J.5
-
24
-
-
84975271190
-
A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators
-
Neugebauer R, Schmittdiel JA, van der Laan MJ. A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators. Int J Biostat 2016;12:131-155.
-
(2016)
Int J Biostat
, vol.12
, pp. 131-155
-
-
Neugebauer, R.1
Schmittdiel, J.A.2
Van Der Laan, M.J.3
-
26
-
-
80053372166
-
Fast linear mixed models for genome-wide association studies
-
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. Fast linear mixed models for genome-wide association studies. Nature Methods 2011;8:833.
-
(2011)
Nature Methods
, vol.8
, pp. 833
-
-
Lippert, C.1
Listgarten, J.2
Liu, Y.3
Kadie, C.M.4
Davidson, R.I.5
Heckerman, D.6
-
27
-
-
79959443658
-
CcSVM: Correcting support vector machines for confounding factors in biological data classification
-
Li L, Rakitsch B, Borgwardt K. CcSVM: Correcting support vector machines for confounding factors in biological data classification. Bioinformatics 2011;27:i342-348.
-
(2011)
Bioinformatics
, vol.27
, pp. i342-i348
-
-
Li, L.1
Rakitsch, B.2
Borgwardt, K.3
-
28
-
-
85045336476
-
Assessing patient risk of central lineassociated bacteremia via machine learning
-
Beeler C, Dbeibo L, Kelley K, et al. Assessing patient risk of central lineassociated bacteremia via machine learning. Am J Infect Control 2018;46:986-991.
-
(2018)
Am J Infect Control
, vol.46
, pp. 986-991
-
-
Beeler, C.1
Dbeibo, L.2
Kelley, K.3
-
29
-
-
85044847275
-
Predicting central line-associated bloodstream infections and mortality using supervised machine learning
-
Parreco JP, Hidalgo AE, Badilla AD, Ilyas O, Rattan R. Predicting central line-associated bloodstream infections and mortality using supervised machine learning. J Crit Care 2018;45:156-162.
-
(2018)
J Crit Care
, vol.45
, pp. 156-162
-
-
Parreco, J.P.1
Hidalgo, A.E.2
Badilla, A.D.3
Ilyas, O.4
Rattan, R.5
-
30
-
-
85056164308
-
Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach
-
Savin I, Ershova K, Kurdyumova N, et al. Healthcare-associated ventriculitis and meningitis in a neuro-ICU: incidence and risk factors selected by machine learning approach. J Crit Care 2018;45:95-104.
-
(2018)
J Crit Care
, vol.45
, pp. 95-104
-
-
Savin, I.1
Ershova, K.2
Kurdyumova, N.3
-
31
-
-
84923013798
-
The rise of big clinical databases
-
Cook JA, Collins GS. The rise of big clinical databases. Br J Surg 2015; 102:e93-e101.
-
(2015)
Br J Surg
, vol.102
, pp. e93-e101
-
-
Cook, J.A.1
Collins, G.S.2
-
32
-
-
84946077182
-
The reporting of studies conducted using observational routinely-collected health data (RECORD) statement
-
Benchimol EI, Smeeth L, Guttmann A, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med 2015;12:e1001885.
-
(2015)
PLoS Med
, vol.12
, pp. e1001885
-
-
Benchimol, E.I.1
Smeeth, L.2
Guttmann, A.3
-
33
-
-
84995810431
-
Extracting information from the text of electronic medical records to improve case detection: A systematic review
-
Ford E, Carroll JA, Smith HE, Scott D, Cassell JA. Extracting information from the text of electronic medical records to improve case detection: a systematic review. J Am Med Inform Assoc 2016;23:1007-1015.
-
(2016)
J Am Med Inform Assoc
, vol.23
, pp. 1007-1015
-
-
Ford, E.1
Carroll, J.A.2
Smith, H.E.3
Scott, D.4
Cassell, J.A.5
-
34
-
-
85027869169
-
Unintended consequences of machine learning in medicine
-
Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017;318:517-518.
-
(2017)
JAMA
, vol.318
, pp. 517-518
-
-
Cabitza, F.1
Rasoini, R.2
Gensini, G.F.3
-
35
-
-
85044927780
-
Big data and machine learning in health care
-
Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018;319:1317-1318.
-
(2018)
JAMA
, vol.319
, pp. 1317-1318
-
-
Beam, A.L.1
Kohane, I.S.2
-
36
-
-
85028364581
-
Multidimensional evidence generation and FDA regulatory decision making: Defining and using "realworld" data
-
Jarow JP, LaVange L, Woodcock J. Multidimensional evidence generation and FDA regulatory decision making: defining and using "realworld" data. JAMA 2017;318:703-704.
-
(2017)
JAMA
, vol.318
, pp. 703-704
-
-
Jarow, J.P.1
LaVange, L.2
Woodcock, J.3
-
37
-
-
84934897215
-
Toward a literature-driven definition of big data in healthcare
-
Baro E, Degoul S, Beuscart R, Chazard E. Toward a literature-driven definition of big data in healthcare. Biomed Res Int 2015;2015:639021.
-
(2015)
Biomed Res Int
, vol.2015
, pp. 639021
-
-
Baro, E.1
Degoul, S.2
Beuscart, R.3
Chazard, E.4
-
38
-
-
84980028545
-
Applying GIS and machine learning methods to twitter data for multiscale surveillance of influenza
-
Allen C, Tsou M-H, Aslam A, Nagel A, Gawron J-M. Applying GIS and machine learning methods to twitter data for multiscale surveillance of influenza. PLoS One 2016;11:e0157734.
-
(2016)
PLoS One
, vol.11
, pp. e0157734
-
-
Allen, C.1
Tsou, M.-H.2
Aslam, A.3
Nagel, A.4
Gawron, J.-M.5
-
39
-
-
85041631179
-
Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
-
Ehrentraut C, Ekholm M, Tanushi H, Tiedemann J, Dalianis H. Detecting hospital-acquired infections: a document classification approach using support vector machines and gradient tree boosting. Health Informatics J 2018;24:24-42.
-
(2018)
Health Informatics J
, vol.24
, pp. 24-42
-
-
Ehrentraut, C.1
Ekholm, M.2
Tanushi, H.3
Tiedemann, J.4
Dalianis, H.5
-
40
-
-
85042731124
-
Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer
-
Kuo P-J, Wu S-C, Chien P-C, et al. Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer. Oncotarget 2018; 9:13768-13782.
-
(2018)
Oncotarget
, vol.9
, pp. 13768-13782
-
-
Kuo, P.-J.1
Wu, S.-C.2
Chien, P.-C.3
-
41
-
-
85048725741
-
Letter to the editor: Predicting central-line-associated bloodstream infections and mortality using supervised machine learning
-
Ferdoash A. Letter to the editor: Predicting central-line-associated bloodstream infections and mortality using supervised machine learning. J Crit Care 2018;46:162.
-
(2018)
J Crit Care
, vol.46
, pp. 162
-
-
Ferdoash, A.1
-
42
-
-
84991259481
-
A prognostic model of surgical site infection using daily clinical wound assessment
-
Sanger PC, van Ramshorst GH, Mercan E, et al. A prognostic model of surgical site infection using daily clinical wound assessment. J Am Coll Surg 2016;223:259-270.
-
(2016)
J Am Coll Surg
, vol.223
, pp. 259-270
-
-
Sanger, P.C.1
Van Ramshorst, G.H.2
Mercan, E.3
-
43
-
-
84959377705
-
A case-based reasoning system for aiding detection and classification of nosocomial infections
-
Gómez-Vallejo HJ, Uriel-Latorre B, Sande-Meijide M, et al. A case-based reasoning system for aiding detection and classification of nosocomial infections. Decision Support Syst 2016;84:104-116.
-
(2016)
Decision Support Syst
, vol.84
, pp. 104-116
-
-
Gómez-Vallejo, H.J.1
Uriel-Latorre, B.2
Sande-Meijide, M.3
-
44
-
-
85041040814
-
Accurate influenza monitoring and forecasting using novel internet data streams: A case study in the Boston metropolis
-
Lu FS, Hou S, Baltrusaitis K, et al. Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston metropolis. JMIR Public Health Surveill 2018;4:e4.
-
(2018)
JMIR Public Health Surveill
, vol.4
, pp. e4
-
-
Lu, F.S.1
Hou, S.2
Baltrusaitis, K.3
-
45
-
-
84946026274
-
Combining search, social media, and traditional data sources to improve influenza surveillance
-
Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 2015; 11:e1004513.
-
(2015)
PLoS Comput Biol
, vol.11
, pp. e1004513
-
-
Santillana, M.1
Nguyen, A.T.2
Dredze, M.3
Paul, M.J.4
Nsoesie, E.O.5
Brownstein, J.S.6
-
46
-
-
84994832127
-
Detection of clinically important colorectal surgical site infection using Bayesian network
-
Sohn S, Larson DW, Habermann EB, Naessens JM, Alabbad JY, Liu H. Detection of clinically important colorectal surgical site infection using bayesian network. J Surg Res 2017;209:168-173.
-
(2017)
J Surg Res
, vol.209
, pp. 168-173
-
-
Sohn, S.1
Larson, D.W.2
Habermann, E.B.3
Naessens, J.M.4
Alabbad, J.Y.5
Liu, H.6
-
47
-
-
85033371762
-
Estimating local costs associated with Clostridium difficile infection using machine learning and electronic medical records
-
Pak TR, Chacko KI, O'Donnell T, et al. Estimating local costs associated with Clostridium difficile infection using machine learning and electronic medical records. Infect Control Hosp Epidemiol 2017;38:1478-1486.
-
(2017)
Infect Control Hosp Epidemiol
, vol.38
, pp. 1478-1486
-
-
Pak, T.R.1
Chacko, K.I.2
O'Donnell, T.3
|