Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons

View More View Less
  • 1 Department of Neurosurgery, Rutgers–Robert Wood Johnson Medical School and University Hospital; and
  • 2 Rutgers Neurosurgery Health Outcomes, Policy, and Economics (HOPE) Center, New Brunswick, New Jersey
Restricted access

Purchase Now

USD  $45.00

JNS + Pediatrics - 1 year subscription bundle (Individuals Only)

USD  $505.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00
Print or Print + Online

Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data–related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.

ABBREVIATIONS AI = artificial intelligence; ANN = artificial neural network; EHR = electronic health record; IoT = Internet of Things.

JNS + Pediatrics - 1 year subscription bundle (Individuals Only)

USD  $505.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $600.00

Contributor Notes

Correspondence Anil Nanda: Rutgers–Robert Wood Johnson Medical School, New Brunswick, NJ. an651@rwjms.rutgers.edu.

INCLUDE WHEN CITING Published online October 2, 2020; DOI: 10.3171/2020.5.JNS201288.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

  • 1

    Alpert J. The electronic medical record in 2016: advantages and disadvantages. Digit Med. 2016;2(2):4851.

  • 2

    Kuo M-H, Sahama T, Kushniruk A, Health big data analytics: current perspectives, challenges and potential solutions. Int J of Big Data Intelligence. 2014;1:114126.

    • Search Google Scholar
    • Export Citation
  • 3

    Wang L, Alexander CA. Big data in medical applications and health care. Am Med J. 2015;6(1):1.

  • 4

    Senthilkumar SA, Rai B, Gunasekaran A. Big data in healthcare management: a review of literature. Am J Theor Appl Bus. 2018;4(2):5769.

    • Search Google Scholar
    • Export Citation
  • 5

    Bydon M, Schirmer CM, Oermann EK, Big data defined: a practical review for neurosurgeons. World Neurosurg. 2020;133:e842e849.

  • 6

    Sun H, Kalakoti P, Sharma K, Proposing a validated clinical app predicting hospitalization cost for extracranial-intracranial bypass surgery. PLoS One. 2017;12(10):e0186758.

    • Search Google Scholar
    • Export Citation
  • 7

    Azimi P, Mohammadi HR, Benzel EC, Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry. 2015;86(3):251256.

  • 8

    Senders JT, Zaki MM, Karhade AV, An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien). 2018;160(1):2938.

    • Search Google Scholar
    • Export Citation
  • 9

    Choi E, Bahadori MT, Schuetz A, Doctor AI: Predicting clinical events via recurrent neural networks. Paper presented at: Machine Learning for Healthcare Conference 2016; Los Angeles, CA. Accessed July 7, 2020. https://arxiv.org/abs/1511.05942

    • Export Citation
  • 10

    Sun H, Samra NS, Kalakoti P, Impact of prehospital transportation on survival in skiers and snowboarders with traumatic brain injury. World Neurosurg. 2017;104:909918.e8.

    • Search Google Scholar
    • Export Citation
  • 11

    Kubben P, Dumontier M, Dekker A, eds. Fundamentals of Clinical Data Science. Springer; 2019.

  • 12

    Jain P, Gyanchandani M, Khare N. Big data privacy: a technological perspective and review. J Big Data. 2016;3(1):25.

  • 13

    Fang R, Pouyanfar S, Yang Y, Computational health informatics in the big data age: a survey. ACM Comput Surv. 2016;49(1):136.

  • 14

    Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2(1):3.

  • 15

    Ebenezer JGA, Durga S. Big data analytics in healthcare: a survey. ARPN J Eng Appl Sci. 2015;10(8):36453650.

  • 16

    Data mining algorithms (analysis services—data mining). Microsoft. May 1, 2018. Accessed August 20, 2020. https://docs.microsoft.com/en-us/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining?view=asallproducts-allversions

    • Export Citation
  • 17

    Brownlee J. A tour of machine learning algorithms. Machine Learning Mastery. August 12, 2019. Accessed July 7, 2020. https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms

    • Search Google Scholar
    • Export Citation
  • 18

    Rughani AI, Dumont TM, Lu Z, Use of an artificial neural network to predict head injury outcome. J Neurosurg. 2010;113(3):585590.

  • 19

    Raj R, Luostarinen T, Pursiainen E, Machine learning-based dynamic mortality prediction after traumatic brain injury. Sci Rep. 2019;9(1):17672.

    • Search Google Scholar
    • Export Citation
  • 20

    Lee CC, Yang HC, Lin CJ, Intervening nidal brain parenchyma and risk of radiation-induced changes after radiosurgery for brain arteriovenous malformation: a study using an unsupervised machine learning algorithm. World Neurosurg. 2019;125:e132e138.

    • Search Google Scholar
    • Export Citation
  • 21

    Scalzo F, Hu X. Semi-supervised detection of intracranial pressure alarms using waveform dynamics. Physiol Meas. 2013;34(4):465478.

  • 22

    Salian I. SuperVize me: What’s the difference between supervised, unsupervised, semi-supervised and reinforcement learning? NVIDIA. August 2, 2018. Accessed July 7, 2020. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning

    • Search Google Scholar
    • Export Citation
  • 23

    Gottesman O, Johansson F, Komorowski M, Guidelines for reinforcement learning in healthcare. Nat Med. 2019;25(1):1618.

  • 24

    Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.

  • 25

    Ramzai J. Simple guide for ensemble learning methods. Towards Data Science. February 26, 2019. Accessed July 7, 2020. https://towardsdatascience.com/simple-guide-for-ensemble-learning-methods-d87cc68705a2

    • Search Google Scholar
    • Export Citation
  • 26

    van Duin S, Bakhshi N. Artificial intelligence defined. Deloitte. Accessed July 7, 2020. https://www2.deloitte.com/se/sv/pages/technology/articles/part1-artificial-intelligence-defined.html

    • Export Citation
  • 27

    Ratner M. FDA backs clinician-free AI imaging diagnostic tools. Nat Biotechnol. 2018;36(8):673674.

  • 28

    Muhlestein WE, Akagi DS, Davies JM, Chambless LB. Predicting inpatient length of stay after brain tumor surgery: developing machine learning ensembles to improve predictive performance. Neurosurgery. 2019;85(3):384393.

    • Search Google Scholar
    • Export Citation
  • 29

    Senders JT, Staples P, Mehrtash A, An online calculator for the prediction of survival in glioblastoma patients using classical statistics and machine learning. Neurosurgery. 2020;86(2):E184E192.

    • Search Google Scholar
    • Export Citation
  • 30

    Hernandes Rocha TA, Elahi C, Cristina da Silva N, A traumatic brain injury prognostic model to support in-hospital triage in a low-income country: a machine learning-based approach. J Neurosurg. 2019;132(6):19611969.

    • Search Google Scholar
    • Export Citation
  • 31

    Staartjes VE, Zattra CM, Akeret K, Neural network–based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. J Neurosurg. 2020;133(2):329335.

    • Search Google Scholar
    • Export Citation
  • 32

    Urbizu A, Martin BA, Moncho D, Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator. J Neurosurg. 2018;129(3):779791.

    • Search Google Scholar
    • Export Citation
  • 33

    Landry AP, Ting WKC, Zador Z, Using artificial neural networks to identify patients with concussion and postconcussion syndrome based on antisaccades. J Neurosurg. 2019;131(4):12351242.

    • Search Google Scholar
    • Export Citation
  • 34

    Huang KT, Silva MA, See AP, A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware. J Neurosurg Spine. 2019;31(6):844850.

    • Search Google Scholar
    • Export Citation
  • 35

    Hopkins BS, Yamaguchi JT, Garcia R, Using machine learning to predict 30-day readmissions after posterior lumbar fusion: an NSQIP study involving 23,264 patients. J Neurosurg Spine. 2020;32(3):399406.

    • Search Google Scholar
    • Export Citation
  • 36

    Burström G, Buerger C, Hoppenbrouwers J, Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. J Neurosurg Spine. 2019;31(1):147154.

    • Search Google Scholar
    • Export Citation
  • 37

    Goyal A, Ngufor C, Kerezoudis P, Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry. J Neurosurg Spine. 2019;31(4):568578.

    • Search Google Scholar
    • Export Citation
  • 38

    Kalagara S, Eltorai AEM, Durand WM, Machine learning modeling for predicting hospital readmission following lumbar laminectomy. J Neurosurg Spine. 2018;30(3):344352.

    • Search Google Scholar
    • Export Citation
  • 39

    Siccoli A, de Wispelaere MP, Schröder ML, Staartjes VE. Machine learning–based preoperative predictive analytics for lumbar spinal stenosis. Neurosurg Focus. 2019;46(5):E5.

    • Search Google Scholar
    • Export Citation
  • 40

    Tunthanathip T, Sae-Heng S, Oearsakul T, Machine learning applications for the prediction of surgical site infection in neurological operations. Neurosurg Focus. 2019;47(2):E7.

    • Search Google Scholar
    • Export Citation
  • 41

    Karhade AV, Ogink P, Thio Q, Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders. Neurosurg Focus. 2018;45(5):E6.

    • Search Google Scholar
    • Export Citation
  • 42

    Paliwal N, Jaiswal P, Tutino VM, Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning. Neurosurg Focus. 2018;45(5):E7.

    • Search Google Scholar
    • Export Citation
  • 43

    Hollon TC, Parikh A, Pandian B, A machine learning approach to predict early outcomes after pituitary adenoma surgery. Neurosurg Focus. 2018;45(5):E8.

    • Search Google Scholar
    • Export Citation
  • 44

    Hale AT, Stonko DP, Wang L, Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging. Neurosurg Focus. 2018;45(5):E4.

    • Search Google Scholar
    • Export Citation
  • 45

    Staartjes VE, Serra C, Muscas G, Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurg Focus. 2018;45(5):E12.

    • Search Google Scholar
    • Export Citation
  • 46

    Scherer M, Cordes J, Younsi A, Development and validation of an automatic segmentation algorithm for quantification of intracerebral hemorrhage. Stroke. 2016;47(11):27762782.

    • Search Google Scholar
    • Export Citation
  • 47

    Lee MH, Kim J, Kim ST, Prediction of IDH1 mutation status in glioblastoma using machine learning technique based on quantitative radiomic data. World Neurosurg. 2019;125:e688e696.

    • Search Google Scholar
    • Export Citation
  • 48

    Nicolaidis S. Personalized medicine in neurosurgery. Metabolism. 2013;62(suppl 1):S45S48.

  • 49

    Ciardiello F, Arnold D, Casali PG, Delivering precision medicine in oncology today and in future—the promise and challenges of personalised cancer medicine: a position paper by the European Society for Medical Oncology (ESMO). Ann Oncol. 2014;25(9):16731678.

    • Search Google Scholar
    • Export Citation
  • 50

    Battelle NeuroLife Neural Bypass Technology. Battelle. Accessed July 7, 2020. https://www.battelle.org/government-offerings/health/medical-devices/neurotechnology/neurolife-neural-bypass-technology

  • 51

    Ghasemi P, Sahraee T, Mohammadi A. Closed- and open-loop deep brain stimulation: methods, challenges, current and future aspects. J Biomed Phys Eng. 2018;8(2):209216.

    • Search Google Scholar
    • Export Citation
  • 52

    Bakkar N, Kovalik T, Lorenzini I, Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol. 2018;135(2):227247.

    • Search Google Scholar
    • Export Citation
  • 53

    The algorithm is in: 5 ways AI is transforming medicine. Inside Battelle. October 19, 2019. Accessed July 7, 2020. https://inside.battelle.org/blog-details/the-algorithm-is-in-5-ways-ai-is-transforming-medicine

    • Export Citation
  • 54

    Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577591.

  • 55

    Radtutor. Accessed July 7, 2020. https://www.radtutor.com

  • 56

    Lillehaug S-I, Lajoie SP. AI in medical education—another grand challenge for medical informatics. Artif Intell Med. 1998;12(3):197225.

    • Search Google Scholar
    • Export Citation
  • 57

    James J. Health policy brief. Pay-for-performance. HealthAffairs. October 11, 2012. Accessed July 7, 2020. https://www.healthaffairs.org/do/10.1377/hpb20121011.90233/full

    • Search Google Scholar
    • Export Citation
  • 58

    Zlojutro A, Rey D, Gardner L. A decision-support framework to optimize border control for global outbreak mitigation. Sci Rep. 2019;9(1):2216.

    • Search Google Scholar
    • Export Citation
  • 59

    Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA. 2020;323(14):13411342.

    • Search Google Scholar
    • Export Citation
  • 60

    Abouelmehdi K, Beni-Hssane A, Khaloufi H, Big data security and privacy in healthcare: a review. Procedia Comput Sci. 2017;113:7380.

  • 61

    Ronquillo JG, Erik Winterholler J, Cwikla K, Health IT, hacking, and cybersecurity: national trends in data breaches of protected health information. JAMIA Open. 2018;1(1):1519.

    • Search Google Scholar
    • Export Citation
  • 62

    Sobers R. 107 must-know data breach statistics for 2020. Varonis. March 29, 2020. Accessed July 7, 2020. https://www.varonis.com/blog/data-breach-statistics/

    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 1883 1883 1883
Full Text Views 217 217 217
PDF Downloads 111 111 111
EPUB Downloads 0 0 0