Skip to main content

Introduction to Machine Learning in Healthcare Informatics

  • Chapter
  • First Online:
Machine Learning in Healthcare Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

Healthcare informatics, a multi-disciplinary field has become synonymous with the technological advancements and big data challenges. With the need to reduce healthcare costs and the movement towards personalized healthcare, the healthcare industry faces changes in three core areas namely, electronic record management, data integration, and computer aided diagnoses. Machine learning a complex field in itself offers a wide range of tools, techniques, and frameworks that can be exploited to address these challenges. This chapter elaborates on the intricacies of data handling the data rich filed of healthcare informatics, and the potential role of machine learning to mitigate the challenges faced.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://www.recovery.gov/About/Pages/The_Act.aspx

  2. 2.

    http://metadata-standards.org/11179/

  3. 3.

    http://www.thehastingscenter.org/Publications/IRB/Default.aspx/

  4. 4.

    http://www.cambio.se/

  5. 5.

    https://www.healthvault.com/us/en

  6. 6.

    http://www.ihtsdo.org/snomed-ct/snomed-ct0/

  7. 7.

    http://www.openehr.org/

References

  1. Zhang Y, Poon C (2010) Editorial note on bio, medical and health informatics. IEEE Trans Inf Technol Biomed 14(3):543–545

    Article  Google Scholar 

  2. Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R (2005) Can electronic medical record systems transform healthcare? potential health benefits, savings, and costs. Health Aff 24(5):1103–1117

    Article  Google Scholar 

  3. Chaudry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton S, Shekelle P (2006) Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annal Internal Med 144, E-12-E-22

    Google Scholar 

  4. Clifton DA, Gibbons J, Davies J, Tarassenko L (2012) Machine learning and software engineering in health informatics. In: First international workshop on realizing artificial intelligence synergies in software engineering (RAISE), Zurich, Switzerland, 5 June 2012

    Google Scholar 

  5. Kerr W, Lau E, Owens G, Trefler A (2012) The future of medical diagnostics: large digitized databases. Yale J Biol Med 85(3):363–377

    Google Scholar 

  6. van Ginneken B, Schaefer-Prokop C, Prokop M (2011) Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261(3):719–732

    Article  Google Scholar 

  7. Bedard N, Pierce M, El-Naggar A, Anandasabapathy S, Gillenwater A, Richards-Kortum R (2010) Emerging roles for multimodal optical imaging in early cancer detection: a global challenge. Technol Cancer Res Treat 9(2):211–217

    Google Scholar 

  8. Weissleder R, Pittet M (2008) Imaging in the era of molecular oncology. Nature 452:580–589

    Article  Google Scholar 

  9. Pierce M, Javier D, Richards-Kortum R (2008) Optical contrast agents and imaging systems for detection and diagnosis of cancer. Int J Cancer 123:1979–1990

    Article  Google Scholar 

  10. Massoud T, Gambhir S (2007) Integrating noninvasive molecular imaging into molecular medicine: an evolving paradigm. Trends Mol Med 13(5):183–191

    Article  Google Scholar 

  11. Suzuki K, Yan P, Wang F, Shen D (2012) Machine learning in medical imaging. Int J Biomed Imaging: Article ID 123727

    Google Scholar 

  12. Richesson R, Nadkarni P (2011) Data standards for clinical research data collection forms: current status and challenges. J Am Med Inform Assoc 18(3):341–346

    Article  Google Scholar 

  13. Oliver DE, Shahar Y, Shortliffe E, Musen M (1999) Representation of change in controlled medical terminologies. Artif Intell Med 15(1):53–76

    Article  Google Scholar 

  14. Chismar W (2007) Introduction to the information technology in healthcare track. In: System sciences, 2007. HICSS 2007. 40th annual Hawaii international conference on, Waikoloa

    Google Scholar 

  15. Ammenwerth E, Gräber S, Herrmann G, Bürkle T, König J (2003) Evaluation of health information systems-problems and challenges. Int J Med Inform 71(2–3):125–135

    Article  Google Scholar 

  16. Salih R, Othmane L, Lilien L (2011) Privacy protection in pervasive healthcare monitoring systems with active bundles. In: Parallel and distributed processing with applications workshops (ISPAW), 2011 ninth IEEE international symposium on, Busan, South Korea, 2011

    Google Scholar 

  17. Yakut I, Polat H (2012) Privacy-preserving hybrid collaborative filtering on cross distributed data. Knowl Inf Syst 30(2):405–433

    Article  Google Scholar 

  18. World Medical Association (2008) WMA declaration of Helsinki - ethical principles for medical research involving human subjects. http://www.wma.net/en/30publications/10policies/b3/. Accessed 10 Feb 2013

  19. Lanham H, Leykum L, McDaniel Jr. R (2012) Same organization, same electronic health records (EHRs) system, different use: exploring the linkage between practice member communication patterns and EHR use patterns in an ambulatory care setting. J Am Med Inform Assoc 19:382–391

    Google Scholar 

  20. Madabhushi A, Agner S, Basavanhally A, Doyle S, Lee G (2011) Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of mult-scale, multi-modal data. Comput Med Imaging Graph 35:506–514

    Article  Google Scholar 

  21. Belle A, Kon M, Najarian K (2013) Biomedical informatics for computer-aided decision support sytems: a survey. Sci World J: Article ID 769639

    Google Scholar 

  22. El-Baz A, Beache G, Gimel’frab G, Suzuki K, Okada K, Elnakib A, Soliman A, Abdollahi B (2012) Computer-Aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging: Article ID 942353

    Google Scholar 

  23. Poon CCY, Wang MD, Bonato P, Fenstermacher DA (2013) Editorial: special issue on health informatics and personalized medicine. Biomed Eng IEEE Trans 60(1):143–146

    Article  Google Scholar 

  24. Fakruddin M, Hossain Z, Afroz H (2012) Prospects and applications fo nanobiotechnology: a medical perspective. J Nanobiotechnol 10(31):1–8

    Google Scholar 

  25. Calhoun B, Lach J, Stankovic J, Wentzloff D, Whitehouse K, Barth A, Brown J, Li Q, Oh S, Roberts N, Zhang Y (2012) Body sensor networks: a holistic approach from silicon to users. Proc IEEE 100(1):91–106

    Article  Google Scholar 

  26. Halamka J, Mandl K, Tang P (2008) Early experiences with personal health records. J Am Med Infom Assoc 15:1–7

    Article  Google Scholar 

  27. Ross S, Lin C (2003) The effects of promoting patient access to medical records: a review. J Am Med Inform Assoc 10:129–138

    Article  Google Scholar 

  28. Hripcsak G, Cimino J, Sengupta S (1999) WebCIS: large scale deployment of a web-based clinical information system. In: Proceedings of AMIA symposium, Washington, DC

    Google Scholar 

  29. Liu Z, Chu W (2007) Knowledge-based query expansion to support scenario-specific retrieval of medical free text. Inf Retieval 10(2):173–202

    Article  MATH  Google Scholar 

  30. Patel C, Cimino J, Dolby J, Fokoue A, Kalyanpur A, Kershenbaum A, Li M, Schonberg E, Srinivas K (2007) Matching patient records to clinical trials using ontologies. Semant Web 4825:816–829

    Google Scholar 

  31. Schloeffel P, Beale T, Hayworth G, Heard S, Leslie H (2006) The relationship between CEN 13606, HL7, and openEHR. In: HIC 2006 bridging the digital divide: clinician, consumer and computer, Australia, health informatics society of Australia Ltd (HISA)

    Google Scholar 

  32. Chen R, Klein G, Sundvall E, Karlsson D, Ahlfeldt H (2009) Archetyoe-based conversion of EHR content models: pilot experience with a regional EHR system. BMC Med Inform Decis Mak 9(33):1–13

    Google Scholar 

  33. Garde S, Knaup P, Hovenga E, Heard S (2007) Towards semantic interoperability for electronic health records: domain knowledge governance for openEHR archetypes. Methods Inf Med 46(3):332–343

    Google Scholar 

  34. Doi K (2007) Computer-Aided diagnosis in medical imaging: historical review, current status, and future potential. Comput Med Imaging Graph 31(4–5):198–211

    Google Scholar 

  35. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39(1):27–34

    Article  Google Scholar 

  36. van Ginneken B, ter Haar Romeny B, Viergever M (2001) Computer-aided diagnosis in chest radiography: a survey. Med Imaging IEEE Trans 20(12):1228–1241

    Google Scholar 

  37. Schorfheide F, Wolpin K (2012) On the use of holdout samples for model selection. Am Econ Rev 102(3):477–481

    Article  Google Scholar 

  38. Padilla P, Lopez M, Gorriz J, Ramirez J, Salas-Gonzalez D, Alvarez I (2012) Alzheimer’s Disease Neuroimaging Initiative NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s Disease. Med Imaging IEEE Trans 31(2):207–216

    Google Scholar 

  39. Dua S, Srinivasan P (2008) A non-voxel bsed feature extraction to detect cognitive states in fMRI. In: 30th annual international IEEE EMBS conference, Vancouver

    Google Scholar 

  40. Kamruzzaman J, Begg R, Sarker R (2006) Overview of artificial neural networks and their applications in healthcare. Neural Networks in Healthcare: Potential and Challenges, Idea Group Inc (IGI), pp 1

    Google Scholar 

  41. Michel V, Gramfort A, Varoquaux G, Eger E, Keribin C, Thirion B (2012) A supervised clustering approach for fMRI-based inference of brain states. Pattern Recogn 45(6):2041–2049

    Article  MATH  Google Scholar 

  42. Lawhern V, Hairston W, McDowell K, Westerfield M, Robbins K (2012) Detection and classification of subject artifacts in EEG signals using autoregressive models. J Neurosci Methods 208(2):181–189

    Article  Google Scholar 

  43. Schalk G, Brunner P, Gerhardt L, Bischof H, Wolpaw JR (2008) Brain–computer interfaces (BCIs): detection instead of classification. J Neurosci Methods 167(1):51–62

    Article  Google Scholar 

  44. Majumdar K (2011) Human scalp EEG processing: various soft computing approaches. Appl Soft Comput 11(8):4433–4447

    Article  Google Scholar 

  45. Ma Z, Tavares J, Jorge R, Mascarenhas T (2010) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235–246

    Article  Google Scholar 

  46. Peters J, Ecabert O, Meyer C, Kneser R, Weese J (2010) Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Med Image Anal 14(1):70

    Article  Google Scholar 

  47. Suk HI, Lee SW (2013) A novel bayesian framework for discriminative feature extraction in brain-computer interfaces. Pattern Anal Mach Learn IEEE Trans 35(2):286–299

    Article  Google Scholar 

  48. Maulik U (2009) Medical image segmentation using genetic algorithms. Inf Technol Biomed IEEE Trans 13(2):166–173

    Article  Google Scholar 

  49. McIntosh C, Hamarneh G (2011) Evolutionary deformable models for medical image segmentation: a genetic algorithm approach to optimizing learned, intuitive, and localized medial‐based shape deformation. In: Stephen L, Smith, Cagnoni S (eds) Genetic and evolutionary computation: Medical Applications, Wiley, pp 46–67

    Google Scholar 

  50. Varol E, Gaonkar B, Erus G, Schultz R, Davatzikos C (2012) Feature ranking based nested support vector machine ensemble for medical image classification. In: 9th IEEE international symposium on biomedical imaging (ISBI)

    Google Scholar 

  51. Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548

    Article  Google Scholar 

  52. Mansi T, Mihalef V, Sharma P, Georgescu B, Zheng X, Rapaka S, Kamen A, Mereles D, Steen H, Meder B, Katus H, Comaniciu D (2012) Data-driven computational models of heart anatomy, mechanics and hemodynamics: an integrated framework. In: 9th IEEE international symposium on biomedical imaging (ISBI)

    Google Scholar 

  53. Mao Y, Chen W, Chen Y, Lu C, Kollef M, Bailey T (2012) An integrated data mining approach to real-time clinical monitoring and deterioration warning. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘12), New York, NY, USA

    Google Scholar 

  54. Caceres C, Rikli A (2012) The digital computer as an aid in the diagnosis of cardiovascular disease. Transactions of the New York Academy of Science. 23(3 series II):240–245

    Google Scholar 

  55. Tracy K, Dykstra B, Gakenheimer D, Scheetz J, Lacina S, Scarfe W, Farman A (2011) Utility and effectiveness of computer-aided diagnosis of dental caries. Gen Dent 59(2):136–144

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pradeep Chowriappa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chowriappa, P., Dua, S., Todorov, Y. (2014). Introduction to Machine Learning in Healthcare Informatics. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40017-9_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics