메뉴 건너뛰기




Volumn 154, Issue 5, 2018, Pages 1239-1248

Big Data and Data Science in Critical Care

Author keywords

big data; critical care; data science; machine learning; prediction models

Indexed keywords

CLINICAL PRACTICE; DIGITALIZATION; EDUCATION PROGRAM; ELECTRONIC HEALTH RECORD; HEALTH CARE SYSTEM; HEALTH SERVICE; HUMAN; IMAGE ANALYSIS; INFORMATION PROCESSING; INTENSIVE CARE; LEARNING ALGORITHM; PRACTICE GUIDELINE; PRIORITY JOURNAL; REVIEW; FORECASTING; HEALTH CARE DELIVERY; ORGANIZATION AND MANAGEMENT; PROCEDURES;

EID: 85051128249     PISSN: 00123692     EISSN: 19313543     Source Type: Journal    
DOI: 10.1016/j.chest.2018.04.037     Document Type: Review
Times cited : (220)

References (59)
  • 1
    • 84867725432 scopus 로고    scopus 로고
    • Best Care at Lower Cost: The Path to Continuously Learning Health Care in America
    • National Academies Press Washington, DC
    • Smith, M., Saunders, R., Stuckhardt, L., McGinnis, J.M., Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. 2013, National Academies Press, Washington, DC.
    • (2013)
    • Smith, M.1    Saunders, R.2    Stuckhardt, L.3    McGinnis, J.M.4
  • 2
    • 84905990877 scopus 로고    scopus 로고
    • Big data in health care: using analytics to identify and manage high-risk and high-cost patients
    • Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G., Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 33:7 (2014), 1123–1131.
    • (2014) Health Aff (Millwood) , vol.33 , Issue.7 , pp. 1123-1131
    • Bates, D.W.1    Saria, S.2    Ohno-Machado, L.3    Shah, A.4    Escobar, G.5
  • 3
    • 85099688310 scopus 로고    scopus 로고
    • Making big data useful for health care: a summary of the inaugural mit critical data conference
    • Badawi, O., Brennan, T., Celi, L.A., et al. Making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR Med Inform, 2(2), 2014, e22.
    • (2014) JMIR Med Inform , vol.2 , Issue.2 , pp. e22
    • Badawi, O.1    Brennan, T.2    Celi, L.A.3
  • 4
    • 84911925260 scopus 로고    scopus 로고
    • What's so different about big data?. A primer for clinicians trained to think epidemiologically
    • Iwashyna, T.J., Liu, V., What's so different about big data?. A primer for clinicians trained to think epidemiologically. Ann Am Thorac Soc 11:7 (2014), 1130–1135.
    • (2014) Ann Am Thorac Soc , vol.11 , Issue.7 , pp. 1130-1135
    • Iwashyna, T.J.1    Liu, V.2
  • 6
    • 84928728572 scopus 로고    scopus 로고
    • State of the art review: the data revolution in critical care
    • Ghassemi, M., Celi, L.A., Stone, D.J., State of the art review: the data revolution in critical care. Crit Care, 19, 2015, 118.
    • (2015) Crit Care , vol.19 , pp. 118
    • Ghassemi, M.1    Celi, L.A.2    Stone, D.J.3
  • 7
    • 84982121501 scopus 로고    scopus 로고
    • Precision medicine for critical illness and injury
    • Buchman, T.G., Billiar, T.R., Elster, E., et al. Precision medicine for critical illness and injury. Crit Care Med 44:9 (2016), 1635–1638.
    • (2016) Crit Care Med , vol.44 , Issue.9 , pp. 1635-1638
    • Buchman, T.G.1    Billiar, T.R.2    Elster, E.3
  • 9
    • 84991818987 scopus 로고    scopus 로고
    • Data science and its relationship to big data and data-driven decision making
    • Provost, F., Fawcett, T., Data science and its relationship to big data and data-driven decision making. Big Data 1:1 (2013), 51–59.
    • (2013) Big Data , vol.1 , Issue.1 , pp. 51-59
    • Provost, F.1    Fawcett, T.2
  • 10
    • 85030466573 scopus 로고    scopus 로고
    • Lost in thought—the limits of the human mind and the future of medicine
    • Obermeyer, Z., Lee, T., Lost in thought—the limits of the human mind and the future of medicine. N Engl J Med, 377(13), 2017, 1209.
    • (2017) N Engl J Med , vol.377 , Issue.13 , pp. 1209
    • Obermeyer, Z.1    Lee, T.2
  • 11
    • 84947466043 scopus 로고    scopus 로고
    • Machine learning in medicine
    • Deo, R.C., Machine learning in medicine. Circulation 132:20 (2015), 1920–1930.
    • (2015) Circulation , vol.132 , Issue.20 , pp. 1920-1930
    • Deo, R.C.1
  • 12
    • 84875646817 scopus 로고    scopus 로고
    • The inevitable application of big data to health care
    • Murdoch, T.B., Detsky, A.S., The inevitable application of big data to health care. JAMA 309:13 (2013), 1351–1352.
    • (2013) JAMA , vol.309 , Issue.13 , pp. 1351-1352
    • Murdoch, T.B.1    Detsky, A.S.2
  • 13
    • 84952778786 scopus 로고    scopus 로고
    • Data science in statistics curricula: preparing students to “think with data.” Am Stat
    • Hardin, J., Hoerl, R., Horton, N.J., et al. Data science in statistics curricula: preparing students to “think with data.” Am Stat., 69(4), 2015, 343–353.
    • (2015) , vol.69 , Issue.4 , pp. 343-353
    • Hardin, J.1    Hoerl, R.2    Horton, N.J.3
  • 15
    • 84954349720 scopus 로고    scopus 로고
    • Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards
    • Churpek, M.M., Yuen, T.C., Winslow, C., Meltzer, D.O., Kattan, M.W., Edelson, D.P., Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44:2 (2016), 368–374.
    • (2016) Crit Care Med , vol.44 , Issue.2 , pp. 368-374
    • Churpek, M.M.1    Yuen, T.C.2    Winslow, C.3    Meltzer, D.O.4    Kattan, M.W.5    Edelson, D.P.6
  • 16
    • 85032875359 scopus 로고    scopus 로고
    • Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness
    • Seymour, C.W., Gomez, H., Chang, C.H., et al. Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness. Crit Care, 21(1), 2017, 257.
    • (2017) Crit Care , vol.21 , Issue.1 , pp. 257
    • Seymour, C.W.1    Gomez, H.2    Chang, C.H.3
  • 17
    • 85041409711 scopus 로고    scopus 로고
    • Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models
    • Mayhew, M.B., Petersen, B.K., Sales, A.P., Greene, J.D., Liu, V.X., Wasson, T.S., Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models. J Biomed Inform 78 (2018), 33–42.
    • (2018) J Biomed Inform , vol.78 , pp. 33-42
    • Mayhew, M.B.1    Petersen, B.K.2    Sales, A.P.3    Greene, J.D.4    Liu, V.X.5    Wasson, T.S.6
  • 18
    • 84973375389 scopus 로고    scopus 로고
    • Combining prognostic and predictive enrichment strategies to identify children with septic shock responsive to corticosteroids
    • Wong, H.R., Atkinson, S.J., Cvijanovich, N.Z., et al. Combining prognostic and predictive enrichment strategies to identify children with septic shock responsive to corticosteroids. Crit Care Med 44:10 (2016), e1000–e1003.
    • (2016) Crit Care Med , vol.44 , Issue.10 , pp. e1000-e1003
    • Wong, H.R.1    Atkinson, S.J.2    Cvijanovich, N.Z.3
  • 19
    • 85010748886 scopus 로고    scopus 로고
    • Tensor factorization for precision medicine in heart failure with preserved ejection fraction
    • Luo, Y., Ahmad, F.S., Shah, S.J., Tensor factorization for precision medicine in heart failure with preserved ejection fraction. J Cardiovasc Transl Res 10:3 (2017), 305–312.
    • (2017) J Cardiovasc Transl Res , vol.10 , Issue.3 , pp. 305-312
    • Luo, Y.1    Ahmad, F.S.2    Shah, S.J.3
  • 21
    • 85007529863 scopus 로고    scopus 로고
    • Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
    • Gulshan, V., Peng, L., Coram, M., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:22 (2016), 2402–2410.
    • (2016) JAMA , vol.316 , Issue.22 , pp. 2402-2410
    • Gulshan, V.1    Peng, L.2    Coram, M.3
  • 22
    • 85016143105 scopus 로고    scopus 로고
    • Dermatologist-level classification of skin cancer with deep neural networks
    • Esteva, A., Kuprel, B., Novoa, R.A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:7639 (2017), 115–118.
    • (2017) Nature , vol.542 , Issue.7639 , pp. 115-118
    • Esteva, A.1    Kuprel, B.2    Novoa, R.A.3
  • 23
    • 84968813824 scopus 로고    scopus 로고
    • Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
    • Miotto, R., Li, L., Kidd, B.A., Dudley, J.T., Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep, 6, 2016, 26094.
    • (2016) Sci Rep , vol.6 , pp. 26094
    • Miotto, R.1    Li, L.2    Kidd, B.A.3    Dudley, J.T.4
  • 24
    • 85055641136 scopus 로고    scopus 로고
    • Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks. arXiv preprint arXiv:170106675. 2017 Jan 23.
    • Aczon M, Ledbetter D, Ho L, et al. Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks. arXiv preprint arXiv:170106675. 2017 Jan 23.
    • Aczon, M.1    Ledbetter, D.2    Ho, L.3
  • 25
    • 85013852123 scopus 로고    scopus 로고
    • Intensive care medicine in 2050: precision medicine
    • Wong, H.R., Intensive care medicine in 2050: precision medicine. Intensive Care Med, 43(10), 2017, 1507.
    • (2017) Intensive Care Med , vol.43 , Issue.10 , pp. 1507
    • Wong, H.R.1
  • 26
    • 0019602768 scopus 로고
    • APACHE-acute physiology and chronic health evaluation: a physiologically based classification system
    • Knaus, W.A., Zimmerman, J.E., Wagner, D.P., Draper, E.A., Lawrence, D.E., APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med 9:8 (1981), 591–597.
    • (1981) Crit Care Med , vol.9 , Issue.8 , pp. 591-597
    • Knaus, W.A.1    Zimmerman, J.E.2    Wagner, D.P.3    Draper, E.A.4    Lawrence, D.E.5
  • 27
    • 0022256529 scopus 로고
    • APACHE II: a severity of disease classification system
    • Knaus, W.A., Draper, E.A., Wagner, D.P., Zimmerman, J.E., APACHE II: a severity of disease classification system. Crit Care Med 13:10 (1985), 818–829.
    • (1985) Crit Care Med , vol.13 , Issue.10 , pp. 818-829
    • Knaus, W.A.1    Draper, E.A.2    Wagner, D.P.3    Zimmerman, J.E.4
  • 28
    • 84921697939 scopus 로고    scopus 로고
    • Multicenter development and validation of a risk stratification tool for ward patients
    • Churpek, M.M., Yuen, T.C., Winslow, C., et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med 190:6 (2014), 649–655.
    • (2014) Am J Respir Crit Care Med , vol.190 , Issue.6 , pp. 649-655
    • Churpek, M.M.1    Yuen, T.C.2    Winslow, C.3
  • 29
    • 84880838139 scopus 로고    scopus 로고
    • Prognostic physiology: modeling patient severity in intensive care units using radial domain folding. Paper presented at: American Medical Informatics Association Annual Symposium Proceedings; November 3-7 Chicago, IL.
    • Joshi R, Szolovits P. Prognostic physiology: modeling patient severity in intensive care units using radial domain folding. Paper presented at: American Medical Informatics Association Annual Symposium Proceedings; November 3-7, 2012; Chicago, IL.
    • (2012)
    • Joshi, R.1    Szolovits, P.2
  • 30
    • 84938704873 scopus 로고    scopus 로고
    • A targeted real-time early warning score (TREWScore) for septic shock
    • 299ra122
    • Henry, K.E., Hager, D.N., Pronovost, P.J., Saria, S., A targeted real-time early warning score (TREWScore) for septic shock. Sci Translat Med, 7(299), 2015 299ra122.
    • (2015) Sci Translat Med , vol.7 , Issue.299
    • Henry, K.E.1    Hager, D.N.2    Pronovost, P.J.3    Saria, S.4
  • 32
    • 84905502770 scopus 로고    scopus 로고
    • Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials
    • Calfee, C.S., Delucchi, K., Parsons, P.E., et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med 2:8 (2014), 611–620.
    • (2014) Lancet Respir Med , vol.2 , Issue.8 , pp. 611-620
    • Calfee, C.S.1    Delucchi, K.2    Parsons, P.E.3
  • 33
    • 84937763158 scopus 로고    scopus 로고
    • Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome
    • Knox, D.B., Lanspa, M.J., Kuttler, K.G., Brewer, S.C., Brown, S.M., Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome. Intensive Care Med 41:5 (2015), 814–822.
    • (2015) Intensive Care Med , vol.41 , Issue.5 , pp. 814-822
    • Knox, D.B.1    Lanspa, M.J.2    Kuttler, K.G.3    Brewer, S.C.4    Brown, S.M.5
  • 34
    • 85007242848 scopus 로고    scopus 로고
    • U Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements. Paper presented at: Proceedings of the 30th AAAI Conference on Artificial Intelligence;
    • Luo Y, Xin Y, Joshi R, Celi L, Szolovits P. Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements. Paper presented at: Proceedings of the 30th AAAI Conference on Artificial Intelligence; 2016.
    • (2016)
    • Luo, Y.1    Xin, Y.2    Joshi, R.3    Celi, L.4    Szolovits, P.5    Predicting, I.C.6
  • 35
    • 85021057838 scopus 로고    scopus 로고
    • Identifying distinct subgroups of ICU patients: a machine learning approach
    • Vranas, K.C., Jopling, J.K., Sweeney, T.E., et al. Identifying distinct subgroups of ICU patients: a machine learning approach. Crit Care Med 45:10 (2017), 1607–1615.
    • (2017) Crit Care Med , vol.45 , Issue.10 , pp. 1607-1615
    • Vranas, K.C.1    Jopling, J.K.2    Sweeney, T.E.3
  • 36
    • 84989356188 scopus 로고    scopus 로고
    • Can you read me now? Unlocking narrative data with natural language processing
    • Sjoding, M.W., Liu, V.X., Can you read me now? Unlocking narrative data with natural language processing. Ann Am Thorac Soc 13:9 (2016), 1443–1445.
    • (2016) Ann Am Thorac Soc , vol.13 , Issue.9 , pp. 1443-1445
    • Sjoding, M.W.1    Liu, V.X.2
  • 37
    • 84880830110 scopus 로고    scopus 로고
    • Risk stratification of ICU patients using topic models inferred from unstructured progress notes. Paper presented at: AMIA annual symposium proceedings;
    • Lehman LW, Saeed M, Long W, Lee J, Mark R. Risk stratification of ICU patients using topic models inferred from unstructured progress notes. Paper presented at: AMIA annual symposium proceedings; 2012.
    • (2012)
    • Lehman, L.W.1    Saeed, M.2    Long, W.3    Lee, J.4    Mark, R.5
  • 38
    • 84907029489 scopus 로고    scopus 로고
    • Unfolding physiological state: mortality modelling in intensive care units. Paper presented at: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;
    • Ghassemi M, Naumann T, Doshi-Velez F, et al. Unfolding physiological state: mortality modelling in intensive care units. Paper presented at: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2014.
    • (2014)
    • Ghassemi, M.1    Naumann, T.2    Doshi-Velez, F.3
  • 39
    • 84989352335 scopus 로고    scopus 로고
    • Natural language processing to assess documentation of features of critical illness in discharge documents of acute respiratory distress syndrome survivors
    • Weissman, G.E., Harhay, M.O., Lugo, R.M., Fuchs, B.D., Halpern, S.D., Mikkelsen, M.E., Natural language processing to assess documentation of features of critical illness in discharge documents of acute respiratory distress syndrome survivors. Ann Am Thoracic Soc 13:9 (2016), 1538–1545.
    • (2016) Ann Am Thoracic Soc , vol.13 , Issue.9 , pp. 1538-1545
    • Weissman, G.E.1    Harhay, M.O.2    Lugo, R.M.3    Fuchs, B.D.4    Halpern, S.D.5    Mikkelsen, M.E.6
  • 40
    • 79955479858 scopus 로고    scopus 로고
    • Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database
    • Saeed, M., Villarroel, M., Reisner, A.T., et al. Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med, 39(5), 2011, 952.
    • (2011) Crit Care Med , vol.39 , Issue.5 , pp. 952
    • Saeed, M.1    Villarroel, M.2    Reisner, A.T.3
  • 41
    • 59649101944 scopus 로고    scopus 로고
    • The cardiac output from blood pressure algorithms trial
    • Sun, J.X., Reisner, A.T., Saeed, M., Heldt, T., Mark, R.G., The cardiac output from blood pressure algorithms trial. Crit Care Med, 37(1), 2009, 72.
    • (2009) Crit Care Med , vol.37 , Issue.1 , pp. 72
    • Sun, J.X.1    Reisner, A.T.2    Saeed, M.3    Heldt, T.4    Mark, R.G.5
  • 42
    • 84953337634 scopus 로고    scopus 로고
    • Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE;
    • Roederer A, Weimer J, DiMartino J, Gutsche J, Lee I. Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE; 2015.
    • (2015)
    • Roederer, A.1    Weimer, J.2    DiMartino, J.3    Gutsche, J.4    Lee, I.5
  • 43
    • 84953322576 scopus 로고    scopus 로고
    • Predicting hyperlactatemia in the MIMIC II database. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE;
    • Dunitz M, Verghese G, Heldt T. Predicting hyperlactatemia in the MIMIC II database. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE; 2015.
    • (2015)
    • Dunitz, M.1    Verghese, G.2    Heldt, T.3
  • 44
    • 85026529300 scopus 로고    scopus 로고
    • A survey on deep learning in medical image analysis
    • Litjens, G., Kooi, T., Bejnordi, B.E., et al. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017), 60–88.
    • (2017) Medical Image Analysis , vol.42 , pp. 60-88
    • Litjens, G.1    Kooi, T.2    Bejnordi, B.E.3
  • 45
    • 85045121335 scopus 로고    scopus 로고
    • Disease staging and prognosis in smokers using deep learning in chest computed tomography
    • Gonzalez, G., Ash, S.Y., Vegas-Sanchez-Ferrero, G., et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med 197:2 (2018), 193–203.
    • (2018) Am J Respir Crit Care Med , vol.197 , Issue.2 , pp. 193-203
    • Gonzalez, G.1    Ash, S.Y.2    Vegas-Sanchez-Ferrero, G.3
  • 46
    • 85055637554 scopus 로고    scopus 로고
    • Deep learning with non-medical training used for chest pathology identification. Paper presented at: Proc. SPIE2015.
    • Bar Y, Diamant I, Wolf L, Greenspan H. Deep learning with non-medical training used for chest pathology identification. Paper presented at: Proc. SPIE2015.
    • Bar, Y.1    Diamant, I.2    Wolf, L.3    Greenspan, H.4
  • 47
    • 84926420669 scopus 로고    scopus 로고
    • Toward a modern era in clinical prediction: the TRIPOD statement for reporting prediction models
    • Tangri, N., Kent, D.M., Toward a modern era in clinical prediction: the TRIPOD statement for reporting prediction models. Am J Kidney Dis 65:4 (2015), 530–533.
    • (2015) Am J Kidney Dis , vol.65 , Issue.4 , pp. 530-533
    • Tangri, N.1    Kent, D.M.2
  • 48
    • 85040255565 scopus 로고    scopus 로고
    • What this computer needs is a physician: humanism and artificial intelligence
    • Verghese, A., Shah, N.H., Harrington, R.A., What this computer needs is a physician: humanism and artificial intelligence. JAMA 319:1 (2018), 19–20.
    • (2018) JAMA , vol.319 , Issue.1 , pp. 19-20
    • Verghese, A.1    Shah, N.H.2    Harrington, R.A.3
  • 49
    • 84924445560 scopus 로고    scopus 로고
    • The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: a pilot step-wedge cluster randomized trial
    • Pickering, B.W., Dong, Y., Ahmed, A., et al. The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: a pilot step-wedge cluster randomized trial. Int J Med Inform 84:5 (2015), 299–307.
    • (2015) Int J Med Inform , vol.84 , Issue.5 , pp. 299-307
    • Pickering, B.W.1    Dong, Y.2    Ahmed, A.3
  • 50
    • 84978933913 scopus 로고    scopus 로고
    • The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial
    • Awdishu, L., Coates, C.R., Lyddane, A., et al. The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial. J Am Med Inform Assoc 23:3 (2016), 609–616.
    • (2016) J Am Med Inform Assoc , vol.23 , Issue.3 , pp. 609-616
    • Awdishu, L.1    Coates, C.R.2    Lyddane, A.3
  • 51
    • 84863332160 scopus 로고    scopus 로고
    • Effect of clinical decision-support systems: a systematic review
    • Bright, T.J., Wong, A., Dhurjati, R., et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 157:1 (2012), 29–43.
    • (2012) Ann Intern Med , vol.157 , Issue.1 , pp. 29-43
    • Bright, T.J.1    Wong, A.2    Dhurjati, R.3
  • 53
    • 85058076047 scopus 로고    scopus 로고
    • Pattern recognition and Machine Learning. New York, NY: Springer Science+Business Media
    • Bishop, C.M., Pattern recognition and Machine Learning. New York, NY: Springer Science+Business Media. LLC, 2006.
    • (2006) LLC
    • Bishop, C.M.1
  • 54
    • 33644699125 scopus 로고    scopus 로고
    • Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system
    • Han, Y.Y., Carcillo, J.A., Venkataraman, S.T., et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 116:6 (2005), 1506–1512.
    • (2005) Pediatrics , vol.116 , Issue.6 , pp. 1506-1512
    • Han, Y.Y.1    Carcillo, J.A.2    Venkataraman, S.T.3
  • 55
    • 84975509985 scopus 로고    scopus 로고
    • Frequency of passive EHR alerts in the ICU: another form of alert fatigue [published online ahead of print June 22, 2016]? J Patient Saf.
    • Kizzier-Carnahan V, Artis KA, Mohan V, Gold JA. Frequency of passive EHR alerts in the ICU: another form of alert fatigue [published online ahead of print June 22, 2016]? J Patient Saf. https://doi.org/10.1097/PTS.0000000000000270.
    • Kizzier-Carnahan, V.1    Artis, K.A.2    Mohan, V.3    Gold, J.A.4
  • 56
    • 84855454549 scopus 로고    scopus 로고
    • Thinking, Fast and Slow
    • Farrar, Staus and Giroux New York, NY
    • Kahneman, D., Thinking, Fast and Slow. 2011, Farrar, Staus and Giroux, New York, NY.
    • (2011)
    • Kahneman, D.1
  • 57
    • 84942259914 scopus 로고    scopus 로고
    • Patient mortality is associated with staff resources and workload in the ICU: a multicenter observational study
    • Neuraz, A., Guerin, C., Payet, C., et al. Patient mortality is associated with staff resources and workload in the ICU: a multicenter observational study. Crit Care Med 43:8 (2015), 1587–1594.
    • (2015) Crit Care Med , vol.43 , Issue.8 , pp. 1587-1594
    • Neuraz, A.1    Guerin, C.2    Payet, C.3
  • 58
    • 85055647811 scopus 로고    scopus 로고
    • Reproducibility in critical care: a mortality prediction case study. Paper presented at: Machine Learning for Healthcare Conference;
    • Johnson AE, Pollard TJ, Mark RG. Reproducibility in critical care: a mortality prediction case study. Paper presented at: Machine Learning for Healthcare Conference; 2017.
    • (2017)
    • Johnson, A.E.1    Pollard, T.J.2    Mark, R.G.3
  • 59
    • 85104094046 scopus 로고    scopus 로고
    • Big data analytics in healthcare: promise and potential
    • Raghupathi, W., Raghupathi, V., Big data analytics in healthcare: promise and potential. Health Inf Sci Syst, 2, 2014, 3.
    • (2014) Health Inf Sci Syst , vol.2 , pp. 3
    • Raghupathi, W.1    Raghupathi, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.