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Introducing Intelligence in Electronic Healthcare Systems: State of the Art and Future Trends

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Artificial Intelligence An International Perspective

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5640))

Abstract

This chapter introduces intelligent technologies applied in electronic healthcare systems and services. It presents an overview of healthcare technologies that enable the advanced patient data acquisition and management of medical information in electronic health records. The chapter presents the most important patient data classification methods, while special focus is placed on new concepts in intelligent healthcare platforms (i.e., advanced data mining, agents and context-aware systems) that provide enhanced means of medical data interpretation and manipulation. The chapter is concluded with the areas in which intelligent electronic healthcare systems are anticipated to make a difference in the near future.

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References

  1. Corchado, J.M., Bajo, J., de Paz, Y., Tapia, D.I.: Intelligent Environment for monitoring Alzheimer patients, agent technology for healthcare. To be published in Decision Support Systems, article available online at http://www.sciencedirect.com

  2. Sharmin, M., Ahmed, S., Ahamed, S.I., Haque, M.M., Khan, A.J.: Healthcare aide: towards a virtual assistant for doctors using pervasive middleware. In: Proc. of Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 6–12 (2006)

    Google Scholar 

  3. Paganelli, F., Spinicci, E., Mamelli, A., Bernazzani, R., Barone, P.: ERMHAN: A multi-channel context-aware platform to support mobile caregivers in continuous care networks. In: Proc. of IEEE International Conference in Pervasive Technologies, pp. 355–360 (2007)

    Google Scholar 

  4. Mitchell, S., Spiteri, M.D., Bates, J., Coulouris, G.: Context-Aware Multimedia Computing in the Intelligent Hospital. In: Proc. SIGOPS EW2000, the Ninth ACM SIGOPS European Workshop (2000)

    Google Scholar 

  5. Hashmi, Z.I., Abidi, S.S.R., Cheah, Y.-N.: An Intelligent Agent-based Knowledge Broker for Enterprise-wide Healthcare Knowledge Procurement. In: 15th IEEE Symposium on Computer-Based Medical Systems (CBMS’02), p. 173 (2002)

    Google Scholar 

  6. Choudhri, A., Kagal, L., Joshi, A., Finin, T., Yesha, Y.: PatientService: Electronic Patient Record Redaction and Delivery in Pervasive Environments. In: Fifth International Workshop on Enterprise Networking and Computing in Healthcare Industry (2003)

    Google Scholar 

  7. Kifor, T., Varga, L., Vazquez-Salceda, J., Alvarez, S., Miles, S., Moreau, L.: Provenance in Agent-Mediated Healthcare Systems. IEEE Intelligent Systems 21(6), 38–46 (2006)

    Article  Google Scholar 

  8. Moreno, A., Valls, A., Isern, D., Sanchez, D.: Applying Agent Technology to Healthcare: The GruSMA Experience. IEEE Intelligent Systems 21(6), 63–67 (2006)

    Article  Google Scholar 

  9. Malan, D., Fulford-Jones, T., Welsh, M., Moulton, S.: CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care. In: International Workshop on Wearable and Implantable Body Sensor Networks (2004)

    Google Scholar 

  10. Gouaux, F., Simon-Chautemps, L., Adami, S., Arzi, M., Assanelli, D., Fayn, J., Forlini, M.C., Malossi, C., Martinez, A., Placide, J., Ziliani, G.L., Rubel, P.: Smart devices for the early detection and interpretation of cardiological syndromes. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 291–294 (2003)

    Google Scholar 

  11. Jeen, Y., Kim, J., Park, J., Park, P.: Design and implementation of the Smart Healthcare Frame Based on Pervasive Computing Technology. In: The 9th International Conference on Advanced Communication Technology, pp. 349–352 (2007)

    Google Scholar 

  12. Camarinha-Matos, L.M., Vieira, W.: Intelligent mobile agents in elderly care. Robotics and Autonomous Systems 27, 59–75 (1999)

    Article  Google Scholar 

  13. Barger, T.S., Brown, D.E., Alwan, M.: Health-Status Monitoring Through Analysis of Behavioral Patterns. IEEE Transactions on Systems, Man and Cybernetics 35(1), 22–27 (2005)

    Article  Google Scholar 

  14. Starida, K., Ganiatsas, G., Fotiadis, D.I., Likas, A.: CHILDCARE: a collaborative environment for the monitoring of children healthcare at home. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 169–172 (2003)

    Google Scholar 

  15. Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Pervasive Health Conference and Workshops, pp. 1–4 (2006)

    Google Scholar 

  16. Jannett, T.C., Prashanth, S., Mishra, S., Ved, V., Mangalvedhekar, A., Deshpande, J.: An intelligent telemedicine system for remote spirometric monitoring. In: Proceedings of the Thirty-Fourth Southeastern Symposium on System Theory, pp. 53–56 (2002)

    Google Scholar 

  17. Dolgov, A.B., Zane, R.: Low-Power Wireless Medical Sensor Platform. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2067–2070 (2006)

    Google Scholar 

  18. Li, H., Tan, J.: Body Sensor Network Based Context Aware QRS Detection. In: Pervasive Health Conference and Workshops, pp. 1–8 (2006)

    Google Scholar 

  19. Demongeot, J., Virone, G., Duchêne, F., Benchetrit, G., Hervé, T., Noury, N., Rialle, V.: Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people. C.R. Biologies 325, 673–682 (2002)

    Article  Google Scholar 

  20. Milenkovic, A., Otto, C., Jovanov, E.: Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer Communications 29, 2521–2533 (2006)

    Article  Google Scholar 

  21. Doukas, C., Maglogiannis, I., Tragas, P., Liapis, D., Yovanof, G.: A Patient Fall Detection System based on Support Vector Machines. In: Proc of 4th IFIP Conference on Artificial Intelligence Applications & Innovations, pp. 147–156 (2007)

    Google Scholar 

  22. Doukas, C., Maglogiannis, I., Kormentzas, G.: Advanced Telemedicine Services through Context-aware Medical Networks. In: International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine (2006)

    Google Scholar 

  23. Lakshmi Narasimhan, V., Irfan, M., Yefremov, M.: MedNet: a pervasive patient information network with decision support. In: 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry, pp. 96–101 (2004)

    Google Scholar 

  24. Mihailidis, A., Carmichael, B., Boger, J.: The Use of Computer Vision in an Intelligent Environment to Support Aging-in-Place, Safety, and Independence in the Home. IEEE Transactions On Information Technology In Biomedicine 8(3), 238–247 (2004)

    Article  Google Scholar 

  25. Choudhri, A., Kagal, L., Joshi, A., Finin, T., Yesha, Y.: PatientService: electronic patient record redaction and delivery in pervasive environments. In: 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry, pp. 41–47 (2003)

    Google Scholar 

  26. Varshney, U.: Pervasive Healthcare. IEEE Computer Magazine 36(12), 138–140 (2003)

    Article  Google Scholar 

  27. Birnbaum, J.: Pervasive information systems. Communications of the ACM 40(2), 40–41 (1997)

    Article  Google Scholar 

  28. Khedo, K.K.: Context-Aware Systems for Mobile and Ubiquitous Networks, International Conference on Networking. In: International Conference on Systems and International Conference on Mobile Communications and Learning Technologies, p. 123 (2006)

    Google Scholar 

  29. Fox, J., Beveridge, M., Glasspool, D.: Understanding intelligent agents: analysis and synthesis. AI Communications 16(3), 139–152 (2003)

    MathSciNet  MATH  Google Scholar 

  30. Zhai, J.-H., Zhang, S.-F., Wang, X.-Z.: An Overview of Pattern Classification Methodologies. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp. 3222–3227 (2006)

    Google Scholar 

  31. Babic, A.: Knowledge Discovery for Advanced Clinical data Management and Analysis. In: Kokol, P., et al. (eds.) Medical Informatics Europe’99, Ljubljana, IOS Press, Amsterdam (1999)

    Google Scholar 

  32. Abidi, S.S.R., Hoe, K.M., Goh, A.: Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, p. 248. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  33. Menachemi, N., Perkins, R.M., van Durme, D.J., Brooks, R.G.: Examining the Adoption of Electronic Health Records and Personal Digital Assistants by Family Physicias in Florida. Informatics In Primary Care 14(1), 8 (2006)

    Google Scholar 

  34. Lærum, H., Karlsen, T.H., Faxvaag, A.: Effects of Scanning and Eliminating Paper-based Medical Records on Hospital Physicians’ Clinical Work Practice. Journal of the American Medical Informatics Association 10, 588–595 (2003)

    Article  Google Scholar 

  35. Wang, S., Middleton, B., Prosser, L.A., Bardon, C.G., Spurr, C.D., Carchidi, P.J., Kittler, A.F., Goldszer, R.C., Fairchild, D.G., Sussman, A.J., Kuperman, G.J., Bates, D.: A cost-benefit analysis of electronic medical records in primary care. Am. J. Med. 114(5), 397–403 (2003)

    Article  Google Scholar 

  36. Hollingsworth, D.: Workflow Management Coalition, The Workflow Reference Model, TC00-1003 (Jan. 1995)

    Google Scholar 

  37. HL7 Standard, http://www.hl7.org

  38. Aniruddha, G., Bharat, K., Arnaud, S.: Reinventing the Wheel? CORBA vs. Web Services, http://www2002.org//CDROM/alternate/395 (visited 11/11/2007)

  39. Dreiseitl, S., Ohno-Machado, L., Kittler, H., Vinterbo, S., Billhardt, H., Binder, M.: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions. Journal of Biomedical Informatics 34, 28–36 (2001)

    Article  Google Scholar 

  40. Muoz, M.A., Rodriguez, M., Favela, J., Martinez-Garcia, A.I., Gonzalez, V.M.: Context aware mobile communication in hospitals. IEEE Computer Magazine 36, 60–67 (2003)

    Article  Google Scholar 

  41. Bardram, J.: Applications of context-aware computing in hospital work: examples and design principles. In: Proc. of the ACM symposium on Applied Computing, pp. 1574–1579 (2004)

    Google Scholar 

  42. Moran, T., Dourish, P.: Introduction to This Special Issue on Context-Aware Computing. Human-Computer Interaction 16(2-4), 87–95 (2001)

    Article  Google Scholar 

  43. Broens, T., van Halteren, A., van Sinderen, M., Wac, K.: Towards an application framework for context-aware m-health applications. In: Proc. of the 11th Open European Summer School (EUNICE 2005), Madrid, Spain, July 6-8 (2005)

    Google Scholar 

  44. Hilario, M., Kalousis, A., Muller, M., Pellegrini, C.: Machine learning approaches to lung cancer prediction from mass spectra. Proteomics 3, 1716–1719 (2003)

    Article  Google Scholar 

  45. Prados, J., Kalousis, A., Sanchez, J.C., Allard, L., Carrette, O., Hilario, M.: Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents. Proteomics 4, 2320–2332 (2004)

    Article  Google Scholar 

  46. Wagner, M., Naik, D., Pothen, A., Kasukurti, S., Devineni, R., Adam, B.L., Semmes, O.J., Wright Jr., G.L.: Computational protein biomarker prediction: a case study for prostate cancer. BMC Bioinformatics 5(26) (2004)

    Google Scholar 

  47. Smith, A.E., Nugent, C.D., McClean, S.I.: Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example. Artificial Intelligence in Medicine 27(1), 1–27 (2003)

    Article  Google Scholar 

  48. Futschik, M.E., Sullivan, M., Reeve, A., Kasabov, N.: Prediction of clinical behaviour and treatment for cancers. OMJ Applied Bioinformatics 2(3), 53–58 (2003)

    Google Scholar 

  49. Ball, G., Mian, S., Holding, F., Allibone, R.O., Lowe, J., Ali, S., Li, G., McCardle, S., Ellis, I.O., Creaser, C., Rees, R.C.: An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics 18(3), 395–404 (2002)

    Article  Google Scholar 

  50. Lancashire, L.J., Mian, S., Ellis, I.O., Rees, R.C., Ball, G.R.: Current developments in the analysis of proteomic data: artificial neural network data mining techniques for the identification of proteomic biomarkers related to breast cancer. Current Proteomics 2(1), 15–29 (2005)

    Article  Google Scholar 

  51. Lisboa, P.J.: A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks 15(1), 11–39 (2002)

    Article  Google Scholar 

  52. Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Berlin (1997)

    Book  MATH  Google Scholar 

  53. Conrads, T.P., Fusaro, V.A., Ross, S., Johann, D., Rajapakse, V., Hitt, B.A., Steinberg, S.M., Kohn, E.C., Fishman, D.A., Whitely, G., Barrett, J.C., Liotta, L.A., Petricoin, E.F., Veenstra, T.D.: High-resolution serum proteomic features for ovarian cancer detection. Endocrine Related Cancer 11(2), 163–178 (2004)

    Article  Google Scholar 

  54. Johann Jr., D.J., McGuigan, M.D., Tomov, S., Fusaro, V.A., Ross, S., Conrads, T.P., Veenstra, T.D., Fishman, D.A., Whiteley, G.R., Petricoin, E.F., Liotta, L.A.: Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients. Disease Markers 19, 197–207 (2004)

    Article  Google Scholar 

  55. Ornstein, D., Rayford, W., Fusaro, V., Conrads, T., Ross, S., Hitt, B., Wiggins, W., Veenstra, T., Liotta, L., Petricoin, E.: Serum Proteomic Profiling Can Discriminate Prostate Cancer From Benign Prostates In Men With Total Prostate Specific Antigen Levels Between 2.5 and 15.0 NG/ML. Journal of Urology 172(4), 1302–1305 (2004)

    Article  Google Scholar 

  56. Stone, J.H., Rajapakse, V.N., Hoffman, G.S., Specks, U., Merkel, P.A., Spiera, R.F., Davis, J.C., St.Clair, E.W., McCune, J., Ross, S., Hitt, B.A., Veenstra, T.D., Conrads, T.P., Liotta, L.A., Petricoin, E.F.: A serum proteomic approach to gauging the state of remission in wegener’s granulomatosis. Arthritis Rheum. 52, 902–910 (2005)

    Article  Google Scholar 

  57. Burges, C.: A tutorial on support vector machines for pattern recognition, http://www.kernel-machines.org/

  58. Christianini, N., Shawe-Taylor, J.: An introduction to support vector machines. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  59. Schölkopf, B.: Statistical learning and kernel methods, http://research.Microsoft.com/~bsc

  60. Statnikov, A., Aliferis, C.F., Tsamardinos, I.: Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development. Medinfo 11, 813–817 (2004)

    Google Scholar 

  61. Li, L., Tang, H., Wu, Z., Gong, J., Gruidl, M., Zou, J., Tockman, M., Clark, R.A.: Data mining techniques for cancer detection using serum proteomic profiling. Artificial Intelligence in Medicine 32, 71–83 (2004)

    Article  Google Scholar 

  62. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K., Zhao, H.: Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19(13), 1636–1643 (2003)

    Article  Google Scholar 

  63. Maglogiannis, I., Pavlopoulos, S., Koutsouris, D.: An Integrated Computer Supported Acquisition, Handling and Characterization System for Pigmented Skin Lesions in Dermatological Images. IEEE Transactions on Information Technology in Biomedicine 9(1), 86–98 (2005)

    Article  Google Scholar 

  64. Maglogiannis, I., Zafiropoulos, E.: Utilizing Support Vector Machines for the Characterization of Digital Medical Images. BMC Medical Informatics and Decision Making 4(4) (2004)

    Google Scholar 

  65. Trimarchi, J.R., Goodside, J., Passmore, L., Silberstein, T., Hamel, L., Gonzalez, L.: Assessing Decision Tree Models for Clinical In-Vitro Fertilization Data. Technical Report TR03-296, Dept. of Computer Science and Statistics, University of Rhode Island (2003)

    Google Scholar 

  66. Niederkohr, R.D., Levin, L.A.: Management of the patient with suspected temporal arteritis a decision-analytic approach. Ophthalmology 112(5), 744–756 (2005)

    Article  Google Scholar 

  67. Ghinea, N., Van Gelder, J.M.: A probabilistic and interactive decision-analysis system for unruptured intracranial aneurysms. Neurosurgical Focus 17(5) (2004)

    Google Scholar 

  68. Markey, M.K., Tourassi, G.D., Floyd, C.E.J.: Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer. Proteomics 3(9), 1678–1679 (2003)

    Article  Google Scholar 

  69. Zhu, H., Yu, C.Y., Zhang, H.: Tree-based disease classification using protein data. Proteomics 3(9), 1673–1677 (2003)

    Article  Google Scholar 

  70. Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: An overview and their use in medicine. Journal of Medical Systems 26(5), 445–463 (2002)

    Article  Google Scholar 

  71. Garg, A.X., Adhikari, N.K., McDonald, H., et al.: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293(10), 1223–1238 (2005)

    Article  Google Scholar 

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  1. University of Central Greece, Papasiopoulou 2-4, PC 35100, Lamia, Greece

    Ilias Maglogiannis

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  1. Ilias Maglogiannis
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  1. School of Computing, Lion Terrace, University of Portsmouth, PO1 3HE, Portsmouth, Hants, UK

    Max Bramer

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Maglogiannis, I. (2009). Introducing Intelligence in Electronic Healthcare Systems: State of the Art and Future Trends. In: Bramer, M. (eds) Artificial Intelligence An International Perspective. Lecture Notes in Computer Science(), vol 5640. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03226-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-03226-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03225-7

  • Online ISBN: 978-3-642-03226-4

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