Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

被引：17

作者：

Chaudhry, Farhan ^{[1
,2
,3
]}

Hunt, Rachel J. ^{[3
]}

Hariharan, Prashant ^{[4
]}

Anand, Sharath Kumar ^{[1
,2
]}

Sanjay, Surya ^{[1
,2
]}

Kjoller, Ellen E. ^{[1
,2
]}

Bartlett, Connor M. ^{[1
,2
]}

Johnson, Kipp W. ^{[5
]}

Levy, Phillip D. ^{[1
,2
]}

Noushmehr, Houtan ^{[3
]}

Lee, Ian Y. ^{[3
]}

机构：

[1] Wayne State Univ, Dept Emergency Med, Detroit, MI 48202 USA

[2] Wayne State Univ, Integrat Biosci Ctr, Detroit, MI 48202 USA

[3] Henry Ford Hosp, Dept Neurosurg, Detroit, MI 48202 USA

[4] Wayne State Univ, Dept Biomed Engn, Detroit, MI USA

[5] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA

来源：

FRONTIERS IN NEUROLOGY | 2020年 / 11卷

关键词：

neurocritical care; machine learning; artificial intelligence; neurology; intensive and critical care; PREDICTION; MODELS; NETWORKS;

D O I：

10.3389/fneur.2020.554633

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.

引用

页数：11

共 54 条

[11] Deep learning for electroencephalogram (EEG) classification tasks: a review [J].

Craik, Alexander ;

He, Yongtian ;

Contreras-Vidal, Jose L. .

JOURNAL OF NEURAL ENGINEERING, 2019, 16 (03)

[12] On the Futility of Blind Search: An Algorithmic View of "No Free Lunch" [J].

Culberson, Joseph C. .

EVOLUTIONARY COMPUTATION, 1998, 6 (02) :109-127

[13] Ensemble methods in machine learning [J].

Dietterich, TG .

MULTIPLE CLASSIFIER SYSTEMS, 2000, 1857 :1-15

[14] The Central Role of Prognosis in Clinical Decision Making [J].

Gill, Thomas M. .

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2012, 307 (02) :199-200

[15] Congenital Aqueductal Stenosis: Findings at Fetal MRI That Accurately Predict a Postnatal Diagnosis [J].

Heaphy-Henault, K. J. ;

Guimaraes, C. V. ;

Mehollin-Ray, A. R. ;

Cassady, C. I. ;

Zhang, W. ;

Desai, N. K. ;

Paldino, M. J. .

AMERICAN JOURNAL OF NEURORADIOLOGY, 2018, 39 (05) :942-948

[16] Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed? [J].

Herta, J. ;

Koren, J. ;

Fuerbass, F. ;

Hartmann, M. ;

Gruber, A. ;

Baumgartner, C. .

CLINICAL NEUROPHYSIOLOGY, 2017, 128 (08) :1524-1531

[17]

Higgins JP, 2002, YALE J BIOL MED, V75, P247

[18] Using AUC and accuracy in evaluating learning algorithms [J].

Huang, J ;

Ling, CX .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (03) :299-310

[19] Taurine and its analogs in neurological disorders: Focus on therapeutic potential and molecular mechanisms [J].

Jakaria, Md ;

Azam, Shofiul ;

Haque, Md Ezazul ;

Jo, Song-Hee ;

Uddin, Md Sahab ;

Kim, In-Su ;

Choi, Dong-Kug .

REDOX BIOLOGY, 2019, 24

[20]

Jia Y., 2014, ACM INT C MULT

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