Skip to main content
SpringerLink
Log in
Book cover

Artificial Intelligence and Data Mining in Healthcare pp 99–118Cite as

An Immune Memory and Negative Selection to Visualize Clinical Pathways from Electronic Health Record Data

  • Chapter
  • First Online:

  • 1023 Accesses

Abstract

Clinical pathways indicate the applicable treatment order of interventions. In this paper we propose a data-driven methodology to extract common clinical pathways from patient-centric electronic health record (EHR) data. The analysis of patients records can lead to better understanding and condoling pathologies. The proposed algorithmic methodology consists of designing a system of control and analysis of patient records based on an analogy between the elements of the new EHRs and the biological immune systems. We use biological immunity to develop a set of models for structuring knowledge extracted from EHR and to make pathway analysis decisions. A specific analysis of the functional data led to the detection of several types of patients who share the same EHR information. This methodology demonstrates its ability to simultaneously process data and is able to provide information for understanding and identifying the path of patients as well as predicting the path of future patients.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. C. Lovis, M. Ball, C. Boyer, P.L. Elkin, K. Ishikawa, C. Jaffe., Hospital and health information systems-current perspectives, Yearb. Med. Inform. (2011) 73–82.

    Google Scholar 

  2. P.G. Goldschmidt, HIT and MIS: implications of health information technology and medical information systems, Commun. ACM 48 (2005) 68–74.

    Article  Google Scholar 

  3. E. Borycki, D. Newsham, D.W. Bates, Health in North America, Yearb. Med. Inform. 103 (2013) 6.

    Google Scholar 

  4. S.S. Jones, R.S. Rudin, T. Perry, P.G. Shekelle, Health information technology: an updated systematic review with a focus on meaningful use, Ann. Int. Med. 160 (2014) 48–54.

    Article  Google Scholar 

  5. D.G. Goldberg, A.J. Kuzel, L.B. Feng, J.P. DeShazo, L.E. Love, EHRs in primary care practices: benefits, challenges and successful strategies, Am. J. Manage. Care. 18 (2012) e48–e54. Fig. 1. The demographics questionnaire screen in the simulation study (the first screen). 38 O. Ben-Assuli et al. / Journal of Biomedical Informatics 55 (2015) 31–40

    Google Scholar 

  6. A. Takian, A. Sheikh, N. Barber, We are bitter, but we are better off: case study of the implementation of an electronic health record system into a mental health hospital in England, BMC Health Serv. Res. 12 (2012) 484.

    Article  Google Scholar 

  7. B. Jarvis, T. Johnson, P. Butler, K. O’Shaughnessy, F. Fullam, L. Tran, et al., Assessing the impact of electronic health records as an enabler of hospital quality and patient satisfaction, Acad. Med.: J. Assoc. Am. Med. Colleges 88 (2013) 1471–1477.

    Google Scholar 

  8. R.D. Cebul, T.E. Love, A.K. Jain, C.J. Hebert, Electronic health records and quality of diabetes care, N Engl. J. Med. 365 (2011) 825–833.

    Article  Google Scholar 

  9. H.S. Sox, M.A. Blatt, M.C. Higgins, K.I. Marton, Medical decision making, American College of Physicians, Philadelphia, Pennsylvania, 2007.

    Google Scholar 

  10. D.A. Redelmeier, E. Shafir, Medical decision making in situations that offer multiple alternatives, J. Am. Med. Assoc. 273 (1995) 302–305.

    Article  Google Scholar 

  11. O. Ben-Assuli, M. Leshno, I. Shabtai, Using electronic medical record systems for admission decisions in emergency departments: examining the crowdedness effect, J. Med. Syst. 36 (2012) 3795–3803.

    Article  Google Scholar 

  12. D.W. Spaite, F. Bartholomeaux, J. Guisto, E. Lindberg, B. Hull, A. Eyherabide, et al., Rapid process redesign in a university-based emergency department: decreasing waiting time intervals and improving patient satisfaction, Ann. Emerg. Med. 39 (2002) 168–177.

    Article  Google Scholar 

  13. S. Schneider, F. Zwemer, A. Doniger, R. Dick, T. Czapranski, E. Davis, Rochester, New York: a decade of emergency department overcrowding, Acad. Emerg. Med. 8 (2001) 1044–1050.

    Article  Google Scholar 

  14. J.B. McCabe, Emergency department overcrowding: a national crisis, Acad. Med. 76 (2001) 672–674.

    Article  Google Scholar 

  15. A. Bair, W. Song, Y.C. Chen, B. Morris, The impact of inpatient boarding on ED efficiency: a discrete-event simulation study, J. Med. Syst. 34 (2010) 919–929.

    Article  Google Scholar 

  16. J. Fordyce, F.S.J. Blank, P. Pekow, H.A. Smithline, G. Ritter, S. Gehlbach, et al., Errors in a busy emergency department, Ann. Emerg. Med. 42 (2003) 324–333.

    Article  Google Scholar 

  17. F. Lecky, J. Benger, S. Mason, P. Cameron, C. Walsh, G. Bodiwala, et al., The international federation for emergency medicine framework for quality and safety in the emergency department, Emerg. Med. J. (2013).

    Google Scholar 

  18. A.E. Lawson, E.S. Daniel, Inferences of clinical diagnostic reasoning and diagnostic error, J. Biomed. Inform. 44 (2011) 402–412.

    Article  Google Scholar 

  19. J. Walker, E. Pan, D. Johnston, J. Adler-Milstein, D.W. Bates, B. Middleton, The value of health care information exchange and interoperability, Health Aff. 24 (2005) 5–10.

    Article  Google Scholar 

  20. G. Hripcsak, S. Sengupta, A. Wilcox, R. Green, Emergency department access to a longitudinal medical record, JAMIA 14 (2007) 235–238.

    Google Scholar 

  21. R.D. Goldman, D. Crum, R. Bromberg, A. Rogovik, J.C. Langer, Analgesia administration for acute abdominal pain in the pediatric emergency department, Pediatr. Emerg. Care 22 (2006) 18–21.

    Article  Google Scholar 

  22. W.R. Hersh, D.H. Hickam, How well do physicians use electronic information retrieval systems? A framework for investigation and systematic review, JAMA 280 (1998) 1347–1352.

    Article  Google Scholar 

  23. J.I. Westbrook, E.W. Coiera, A.S. Gosling, Do online information retrieval systems help experienced clinicians answer clinical questions?, J Am. Med. Inform. Assoc. 12 (2005) 315–321.

    Article  Google Scholar 

  24. J.I. Westbrook, A.S. Gosling, E.W. Coiera, The impact of an online evidence system on confidence in decision making in a controlled setting, Med. Decis. Making 25 (2005) 178–185.

    Article  Google Scholar 

  25. W. Hersh, M. Helfand, J. Wallace, D. Kraemer, P. Patterson, S. Shapiro, et al., A systematic review of the efficacy of telemedicine for making diagnostic and management decisions, J. Telemed. Telecare 8 (2002) 197–209.

    Article  Google Scholar 

  26. D. Blumenthal, M. Tavenner, The, “meaningful use” regulation for electronic health records, N Engl. J. Med. 363 (2010) 501–504.

    Article  Google Scholar 

  27. H. Laerum, T.H. Karlsen, A. Faxvaag, Effects of scanning and eliminating paper-based medical records on hospital physicians’ clinical work practice, JAMIA 10 (2003) 588–595.

    Google Scholar 

  28. M.E. Johnston, K.B. Langton, R.B. Haynes, A. Mathieu, Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research, Ann. Int. Med. 120 (1994) 135–142.

    Article  Google Scholar 

  29. V.L. Smith-Daniels, S.B. Schweikhart, D.E. Smith-Daniels, Capacity management in health care services: review and future research directions, Decision Sci. 19 (1988) 889–919.

    Article  Google Scholar 

  30. K.K. Sinha, E.J. Kohnke, Health care supply chain design: toward linking the development and delivery of care globally, Decision Sci. 40 (2009) 197–212.

    Article  Google Scholar 

  31. W. Michalowski, M. Kersten, S. Wilk, R. Słowinski, Designing man-machine interactions for mobile clinical systems: MET triage support using Palm handhelds, EJOR 177 (2007) 1409–1417.

    Article  MATH  Google Scholar 

  32. G. Xue, Z. Lu, I.P. Levin, A. Bechara, The impact of prior risk experiences on subsequent risky decision-making: the role of the insula, NeuroImage 50 (2010) 709–716.

    Article  Google Scholar 

  33. G. Gigerenzer, W. Gaissmaier, Heuristic decision making, Annu. Rev. Psychol. 62 (2011) 451–482.

    Article  Google Scholar 

  34. S.F. Wainwright, K.F. Shepard, L.B. Harman, J. Stephens, Factors that influence the clinical decision making of novice and experienced physical therapists, Phys. Ther. 91 (2011) 87–101.

    Article  Google Scholar 

  35. R.A. Greenes, E.H. Shortliffe, Commentary: Informatics in biomedicine and health care, Acad. Med. 84 (2009) 818–820.

    Article  Google Scholar 

  36. M.A. Musen, B. Middleton, R.A. Greenes, Clinical decision – support systems, in: E.H. Shortliffe, J.J. Cimino (Eds.), Biomedical Informatics, Springer, London,(2014), pp. 643–674.

    Chapter  Google Scholar 

  37. E. Dyer, V. Mohan, J.A. Gold, K.A. Artis, Data communication errors and the electronic health record in the intensive care unit: a pilot study, Am. J. Respir. Crit. Care Med. 189 (2014) 2803.

    Google Scholar 

  38. O. Ben-Assuli, M. Leshno, Using electronic medical records in admission decisions: a cost effectiveness analysis, Decision Sci. 44 (2013) 463–481.

    Article  Google Scholar 

  39. A.S. McAlearney, J.L. Hefner, C. Sieck, M. Rizer, T.R. Huerta, Evidence-based management of ambulatory electronic health record system implementation: an assessment of conceptual support and qualitative evidence, Int. J. Med. Inform. 83 (2014) 484–494.

    Article  Google Scholar 

  40. E.H. Shortliffe, Computer programs to support clinical decision making, JAMA 258 (1987) 61–66.

    Article  Google Scholar 

  41. J. Wyatt, D. Spiegelhalter, Evaluating Medical Expert Systems: What to Test, and How? Knowledge Based Systems in Medicine: Methods, Applications and Evaluation, Springer, (1991) 274–90.

    Google Scholar 

  42. C. Heath, R. Gonzalez, Interaction with others increases decision confidence but not decision quality: evidence against information collection views of interactive decision making, Organ. Behav. Hum. Decis. Process. 61 (1995) 305–326.

    Article  Google Scholar 

  43. A. Zylberberg, P. Barttfeld, M. Sigman, The construction of confidence in a perceptual decision, Front. Integr. Neurosci. 6 (2012) 1–10.

    Article  Google Scholar 

  44. O. Ben-Assuli, I. Shabtai, M. Leshno, The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses, BMC Med. Inform. Decis. Mak. 13 (2013) 49.

    Article  Google Scholar 

  45. W.R. Hersh, M.K. Crabtree, D.H. Hickam, L. Sacherek, C.P. Friedman, P. Tidmarsh, et al., Factors associated with success in searching MEDLINE and applying evidence to answer clinical questions, JAMIA 9 (2002) 283–293.

    Google Scholar 

  46. A.D. Black, J. Car, C. Pagliari, C. Anandan, K. Cresswell, T. Bokun, et al., The impact of eHealth on the quality and safety of health care: a systematic overview, PLoS Med. 8 (2011) e1000387.

    Article  Google Scholar 

  47. A. Basu, D. Meltzer, Value of information on preference heterogeneity and individualized care, Med. Decis. Making 27 (2007) 112–127.

    Article  Google Scholar 

  48. H.E. Rippen, E.C. Pan, C. Russell, C.M. Byrne, E.K. Swift, Organizational framework for health information technology, Int. J. Med. Inform. 82 (2013) e1–e13.

    Article  Google Scholar 

  49. K. Claxton, M. Sculpher, M. Drummond, A rational framework for decision making by the National Institute for Clinical Excellence (NICE), Lancet 360 (2002) 711–715.

    Article  Google Scholar 

  50. B. Kapoor, M. Kleinbart, Building an integrated patient information system for a healthcare network, J. Cases Inform. Technol. 14 (2012) 27–41.

    Google Scholar 

  51. E.H. Wagner, T. Bledsoe, The rand health insurance experiment and HMOs, Med. Care 28 (1990) 191–200.

    Article  Google Scholar 

  52. J. Schmittdiel, J.V. Selby, K. Grumbach, C.P. Quesenberry, Choice of a personal physician and patient satisfaction in a health maintenance organization, JAMA 278 (1997) 1596–1599.

    Article  Google Scholar 

  53. L. Lapointe, S. Rivard, A multilevel model of resistance to information technology implementation, MIS Quart. 29 (2005) 461–491.

    Article  Google Scholar 

  54. R.J. Holden, B.T. Karsh, The technology acceptance model: its past and its future in health care, J. Biomed. Inform. 43 (2010) 159–172.

    Article  Google Scholar 

  55. C.E. Bartos, B.S. Butler, R.S. Crowley, Ranked levels of influence model: selecting influence techniques to minimize IT resistance, J. Biomed. Inform. 44 (2010) 497–504.

    Article  Google Scholar 

  56. J.L.Y. Liu, J.C. Wyatt, The case for randomized controlled trials to assess the impact of clinical information systems, JAMA 18 (2011) 173–180.

    Google Scholar 

  57. M. Bloomrosen, J. Starren, N.M. Lorenzi, J.S. Ash, V.L. Patel, E.H. Shortliffe, Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 health policy meeting, JAMIA 18 (2011) 82–90.

    Google Scholar 

  58. M. Peleg, J. Somekh, D. Dori, A methodology for eliciting and modeling exceptions, J. Boimed. Inform. 42 (2009) 736–747.

    Article  Google Scholar 

  59. J.R. Vest, J. Jasperson, What should we measure? Conceptualizing usage in health information exchange, JAMIA 17 (2010) 302–307.

    Google Scholar 

  60. U. Varshney, Monitoring of Mental Health, Medication and Disability. Pervasive Healthcare Computing: EMR/EHR, Wireless and Health Monitoring. Georgia State University, Springer, Atlanta, GA, USA, 2009, pp. 259–280.

    Google Scholar 

  61. E. Borycki, R.S. Joe, B. Armstrong, P. Bellwood, R. Campbell, Educating health professionals about the electronic health record (EHR): removing the barriers to adoption, Knowledge Manage. E-Learn.: Int. J. 3 (2011) 51–62.

    Google Scholar 

  62. K.W. Hammond, E.N. Efthimiadis, C.R. Weir, P.J. Embi, S.M. Thielke, R.M. Laundry et al., Initial steps toward validating and measuring the quality of computerized provider documentation, in: AMIA Annual Symposium Proceedings: American Medical Informatics Association, 2010, p. 271.

    Google Scholar 

  63. X. Jing, S. Kay, T. Marley, N.R. Hardiker, J.J. Cimino, Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard, J. Biomed. Inform. 45 (2012) 82–92.

    Article  Google Scholar 

  64. L. N. De Castro, J. I. Timmis, Artificial immune systems: A new computational intelligence approach. Springer – Verlag, Berlin, 2002.

    MATH  Google Scholar 

  65. M. Berquedich, O. Kamach, M. Masmoudi, An Immune Memory and Negative Selection to Manage Tensions in Emergency Services. International Journal of Intelligent Engineering and Systems, 12 (2019) 214–228.

    Article  Google Scholar 

  66. S. Forrest, A.S. Perelson, L. Allen, R. Cherukuri, Self-nonself discrimination in a computer, in: IEEE Symposium on Research in Security and Privacy, IEEE Computer Society Press, Los Alamos, CA, 1994.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Innovative Technologies (LTI), Abdelmalek Saâdi University, Tangier, Morocco

    Mouna Berquedich & Oulaid Kamach

  2. Innovation Lab for Operations (ILO), University Mohammed 6 Polytechnic, Ben Guerir, Morocco

    Mouna Berquedich & Laurent Deshayes

  3. University of Jean-Monnet, Faculty of Sciences and Technologies, Saint-Étienne, France

    Malek Masmoudi

Authors
  1. Mouna Berquedich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oulaid Kamach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malek Masmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laurent Deshayes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouna Berquedich .

Editor information

Editors and Affiliations

  1. LASPI, G037, IUT de Roanne, Roanne, France

    Malek Masmoudi

  2. Department of Business, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Bassem Jarboui

  3. Laboratoire LiSSi (EA 3956), Université Paris-Est Créteil Val-de-Marne, Créteil, France

    Patrick Siarry

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Berquedich, M., Kamach, O., Masmoudi, M., Deshayes, L. (2021). An Immune Memory and Negative Selection to Visualize Clinical Pathways from Electronic Health Record Data. In: Masmoudi, M., Jarboui, B., Siarry, P. (eds) Artificial Intelligence and Data Mining in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-030-45240-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-45240-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45239-1

  • Online ISBN: 978-3-030-45240-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation