Skip to main content
SpringerLink
Log in

Machine Learning and Artificial Intelligence

  • Chapter
  • First Online:
Health Services Research

Part of the book series: Success in Academic Surgery ((SIAS))

Abstract

Interest in artificial intelligence for use in healthcare and health services research is growing as the amount of data available is ever increasing. In this chapter, we define the terminology surrounding artificial intelligence. Machine learning methods are the building blocks for artificial intelligence, and we provide an overview of selected methods useful in healthcare and health services research. Cutting edge “deep learning” hold particular promise for image analysis and natural language processing. We review two examples to illustrate the features of deep learning that make it a powerful tool for research and clinical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol. 2010;105(6):1224–6.

    Article  Google Scholar 

  2. Lyman GH, Balducci L. Overestimation of test effects in clinical judgment. J Cancer Educ. 1993;8(4):297–307.

    Article  CAS  Google Scholar 

  3. Obermeyer Z, Emanuel EJ. Predicting the future— Big Data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9.

    Article  Google Scholar 

  4. Witten IH, Frank E. Data mining: practical machine learning tools and techniques. San Francisco, CA: Elsevier; 2005.

    Google Scholar 

  5. Hindman M. Building better models: prediction, replication, and machine learning in the social sciences. Ann Am Acad Pol Soc Sci. 2015;659(1):48–62.

    Article  Google Scholar 

  6. Brennan PF, Aronson AR. Towards linking patients and clinical information: detecting UMLS concepts in e-mail. J Biomed Inform. 2003;36(4–5):334–41.

    Article  Google Scholar 

  7. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17(5):507–13.

    Article  Google Scholar 

  8. Kulikowski CA, Shortliffe EH, Currie LM, Elkin PL, Hunter LE, Johnson TR, et al. AMIA board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline. J Am Med Inform Assoc. 2012;19(6):931–8.

    Article  Google Scholar 

  9. Friedman CP. What informatics is and isn't. J Am Med Inform Assoc. 2013;20(2):224–6.

    Article  Google Scholar 

  10. Kotsiantis SB. Supervised machine learning: a review of classification techniques. Informatica. 2007;31:249–68.

    Google Scholar 

  11. Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507–9.

    Article  Google Scholar 

  12. Tobler JB, Molla MN, Nuwaysir EF, Green RD, Shavlik JW. Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. Bioinformatics. 2002;18(Suppl 1):S164–71.

    Article  Google Scholar 

  13. Chipman HA, George EI, McCulloch RE. Bayesian CART model search. J Am Stat Assoc. 1998;93:935–60.

    Article  Google Scholar 

  14. Biggs D, de Ville B, Suen B. A method of choosing multiway partitions for classification and decision trees. J Appl Stat. 1991;18:49–62.

    Article  Google Scholar 

  15. Schneider DF, Dobrowolsky A, Shakir IA, Sinacore JM, Mosier MJ, Gamelli RL. Predicting acute kidney injury among burn patients in the 21st century: a classification and regression tree analysis. J Burn Care Res. 2012;33(2):242–51.

    Article  Google Scholar 

  16. Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, et al., editors. Recursive deep models for semantic compositionality over a sentiment tree-bank. 2013 Conference on Empirical Methods in Natural Language Processing; 2013

    Google Scholar 

  17. Burnside ES, Rubin DL, Fine JP, Shachter RD, Sisney GA, Leung WK. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology. 2006;240(3):666–73.

    Article  Google Scholar 

  18. Kahn CE Jr, Roberts LM, Wang K, Jenks D, Haddawy P. Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. Proc Annu Symp Comput Appl Med Care. 1995:208–12.

    Google Scholar 

  19. Liu YI, Kamaya A, Desser TS, Rubin DLA. Bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features. AMIA Annu Symp Proc. 2008:419–23.

    Google Scholar 

  20. Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery. 2017;161(4):1113–21.

    Article  Google Scholar 

  21. D'Avolio LW, Nguyen TM, Goryachev S, Fiore LD. Automated concept-level information extraction to reduce the need for custom software and rules development. J Am Med Inform Assoc. 2011;18(5):607–13.

    Article  Google Scholar 

  22. Imbus JR, Randle RW, Pitt SC, Sippel RS, Schneider DF. Machine learning to identify multigland disease in primary hyperparathyroidism. J Surg Res. 2017;219:173–9.

    Article  Google Scholar 

  23. Dhawan A, Wenzel B, George S, Gussak I, Bojovic B, Panescu D. Detection of acute myocardial infarction from serial ECG using multilayer support vector machine. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:2704–7.

    PubMed  Google Scholar 

  24. Kim S. Weighted K-means support vector machine for cancer prediction. Springerplus. 2016;5(1):1162.

    Article  Google Scholar 

  25. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.

    Article  Google Scholar 

  26. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  CAS  Google Scholar 

  27. Ahn H, Moon H, Fazzari MJ, Lim N, Kodell RL. Classification by ensembles from random partitions of high-dimensional data. Comput Stat Data Anal. 2007;51:6166–79.

    Article  Google Scholar 

  28. Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med. 2011;41(6):449–62.

    Article  Google Scholar 

  29. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  Google Scholar 

  30. Do CB, Ng AY, editors. Transfer learning for text classification. Neural information processing systems (NIPS). Vancouver, British Columbia, Canada: Neural Information Processing Systems Foundation; 2005.

    Google Scholar 

  31. Raina R, Ng AY, Koller D editors, Constructing Informative Priors using Transfer Learning. 23rd International Conference on Machine Learning; 2006; Pittsburgh, PA.

    Google Scholar 

  32. Hazlehurst B, Sittig DF, Stevens VJ, Smith KS, Hollis JF, Vogt TM, et al. Natural language processing in the electronic medical record: assessing clinician adherence to tobacco treatment guidelines. Am J Prev Med. 2005;29(5):434–9.

    Article  Google Scholar 

  33. Hripcsak G, Austin JH, Alderson PO, Friedman C. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology. 2002;224(1):157–63.

    Article  Google Scholar 

  34. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–51.

    Article  Google Scholar 

  35. Zhou L, Tao Y, Cimino JJ, Chen ES, Liu H, Lussier YA, et al. Terminology model discovery using natural language processing and visualization techniques. J Biomed Inform. 2006;39(6):626–36.

    Article  Google Scholar 

  36. Mikolov T, Chen K, Corrado G, Dean D, editors. Efficient estimation of word representations in vector space. International Conference on Learning Representations Workshop; 2013; Scottsdale, AZ, 2013.

    Google Scholar 

  37. Pennington J, Socher R, Manning CD, editors. GloVe: Global vectors for word representation. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014); 2014; Doha, Qatar.

    Google Scholar 

  38. Sarma P, Liang Y, Sethares W. Domain adapted word embeddings for improved sentiment classification. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018); July 15–20, 2018; Melbourne, Australia 2018.

    Google Scholar 

  39. Sarma P, Sethares W. Simple algorithms for sentiment analysis on sentiment rich, data poor domains. 27th International Conference on Computational Linguistics (COLING 2018); August 2018; Santa Fe, New Mexico 2018.

    Google Scholar 

  40. Kim Y, editor Convolutional neural networks for sentence classification. Conference on Empirical Methods in Natural Language Processing (EMNLP); 2014 October 25–29, 2014; Doha, Qatar: Association for Computational Linguistics.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    David F. Schneider

Authors
  1. David F. Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David F. Schneider .

Editor information

Editors and Affiliations

  1. Department of Surgery, University of Michigan Health System, Ann Arbor, MI, USA

    Justin B. Dimick

  2. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Carrie C. Lubitz

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Schneider, D.F. (2020). Machine Learning and Artificial Intelligence. In: Dimick, J., Lubitz, C. (eds) Health Services Research. Success in Academic Surgery. Springer, Cham. https://doi.org/10.1007/978-3-030-28357-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-28357-5_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28356-8

  • Online ISBN: 978-3-030-28357-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation