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International Conference on Smart Health

ICSH 2018: Smart Health pp 26–40Cite as

The Design of Personalized Artificial Intelligence Diagnosis and the Treatment of Health Management Systems Simulating the Role of General Practitioners

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10983))

Abstract

Artificial Intelligence (AI) has continuously been used as a method in the fields of medical and clinical research to improve patients’ health outcomes. However, the evidence of its effectiveness in self-health management through strengthening one’s subconscious mind to change his/her health behavior is not well supported. This paper will use a design science method to describe The Design of Personalized Artificial Intelligence Diagnosis and the Treatment of Health Management Systems Simulating the Role of General Practitioners (AIHMS) that assists in providing tailored interventions to enhance health related behavioral changes. Findings from AI healthcare studies have shown promising insights, particularly in improving self-management and some health outcomes. In fact, AIHMS has not only promoted the happiness of patients, but eased the relationship between doctors and patients, improved patient’s satisfaction and other benefits, with far-reaching theoretical and practical implications. Furthermore, AI technology service innovation will improve the wellbeing of patients.

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References

  • Bansal, G., Zahedi, F.M., Gefen, D.: The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis. Support Syst. 49(2), 138–150 (2010)

    Article  Google Scholar 

  • Johnston, A.C., Warkentin, M.: Fear appeals and information security behaviors: an empirical study. MIS Q. 34(3), 549–566 (2010)

    Article  Google Scholar 

  • Horne, R., Weinman, J.: Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. J. Psychosom. Res. 47(6), 555 (1999)

    Article  Google Scholar 

  • Ghimire, S., Castelino, R.L., Jose, M.D., Zaidi, S.T.R.: Medication adherence perspectives in haemodialysis patients: a qualitative study. BMC Nephrol. 18(1), 167 (2017)

    Article  Google Scholar 

  • Bardram, J.E.: Pervasive healthcare as a scientific discipline. Methods Inf. Med. 47(3), 178–185 (2008)

    Article  Google Scholar 

  • Lee, T.S.: Present state and prospects of mobile healthcare. In: Proceedings of KIEE, vol. 53, no. 9, pp. 36–42 (2004)

    Google Scholar 

  • Kang, S.M., Kim, M.J., Ahn, H.Y., et al.: Ubiquitous healthcare service has the persistent benefit on glycemic control and body weight in older adults with diabetes. Diabetes Care 35(3), e19 (2012)

    Article  Google Scholar 

  • Lee, T.S., Hong, J.H., Cho, M.C.: Biomedical digital assistant for ubiquitous healthcare. In: International Conference of the IEEE Engineering in Medicine and Biology Society, p. 1790 (2007)

    Google Scholar 

  • Milani, R.V., Bober, R.M., Lavie, C.J.: The role of technology in chronic disease care. Prog. Cardiovasc. Dis. 58(6), 579–583 (2016)

    Article  Google Scholar 

  • Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)

    Article  Google Scholar 

  • Min, T.: Application research of wechat robot in real-time virtual reference service in the library: taking shanghai minhang district library as an example. New Century Libr. (2015)

    Google Scholar 

  • Kwakkel, G., Kollen, B.J., Krebs, H.I.: Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation Neural Repair 22(2), 111 (2008)

    Article  Google Scholar 

  • Lo, A.C., Guarino, P.D., Richards, L.G., et al.: Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362(19), 1772–1783 (2010)

    Article  Google Scholar 

  • Odusola, A.O., Hendriks, M., Schultsz, C., et al.: Perceptions of inhibitors and facilitators for adhering to hypertension treatment among insured patients in rural Nigeria: a qualitative study. BMC Health Serv. Res. 14(1), 1–16 (2014)

    Article  Google Scholar 

  • Kim, H.S., Cho, J.H., Yoon, K.H.: New directions in chronic disease management. Endocrinol. Metab. 30(2), 159–166 (2015)

    Article  Google Scholar 

  • Bellos, C., Papadopoulos, A., Rosso, R., et al.: Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011(4), 8303–8306 (2011)

    Google Scholar 

  • Sobrinho, Á.A.D.C.C., Silva, L.D.D., Medeiros, L.M.D.: MultCare a mobile assistant as a tool to aid early detection of chronic kidney disease. Procedia Technol. 5, 830–838 (2012)

    Article  Google Scholar 

  • Fadhil, A., Gabrielli, S.: Addressing challenges in promoting healthy lifestyles: the al-chatbot approach. In: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 261–265. ACM (2017)

    Google Scholar 

  • Frank, E.: Physician health and patient care. JAMA 291(5), 637 (2004)

    Article  Google Scholar 

  • Weizenbaum, J.: ELIZA—a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)

    Article  Google Scholar 

  • 陈建伟: 人工智能与医疗深度融合. 中国卫生 (9), 102–103 (2017)

    Google Scholar 

  • Mohr, D.C., Dick, L.P., Russo, D., et al.: The psychosocial impact of multiple sclerosis: exploring the patient’s perspective. Health Psychol. Off. J. Div. Health Psychol. Am. Psychol. Assoc. 18(4), 376–382 (1999a)

    Google Scholar 

  • Mohr, D.C., Goodkin, D.E., Likosky, W., et al.: Therapeutic expectations of patients with multiple sclerosis upon initiating interferon beta-1b: relationship to adherence to treatment. Mult. Scler. 2(5), 222 (1996)

    Article  Google Scholar 

  • Mohr, D.C., Goodkin, D.E., Likosky, W., et al.: Treatment of depression improves adherence to interferon beta-1b therapy for multiple sclerosis. Arch. Neurol. 54(5), 531 (1997)

    Article  Google Scholar 

  • Mohr, D.C., Likosky, W., Boudewyn, A.C., et al.: Side effect profile and adherence to in the treatment of multiple sclerosis with interferon beta-1a. Mult. Scler. J. 4(6), 487–489 (1998)

    Google Scholar 

  • Mohr, D.C., Goodkin, D.E., Masuoka, L., et al.: Treatment adherence and patient retention in the first year of a Phase-III clinical trial for the treatment of multiple sclerosis. Mult. Scler. J. 5(3), 192–197 (1999b)

    Article  Google Scholar 

  • Mohr, D.C., Likosky, W., Bertagnolli, A., et al.: Telephone-administered cognitive–behavioral therapy for the treatment of depressive symptoms in multiple sclerosis. J. Consult. Clin. Psychol. 68(2), 356–361 (2000)

    Article  Google Scholar 

  • Mohr, D.C., Boudewyn, A.C., Likosky, W., et al.: Injectable medication for the treatment of multiple sclerosis: the influence of self-efficacy expectations and infection anxiety on adherence and ability to self-inject. Ann. Behav. Med. 23(2), 125–132 (2001)

    Article  Google Scholar 

  • Fogg, B.J.: Persuasive technologies. Commun. ACM 42(5), 26–29 (1999)

    Article  Google Scholar 

  • Kim, E., Kim, W., Lee, Y.: Combination of multiple classifiers for the customer’s purchase behavior prediction. Decis. Support Syst. 34(2), 167–175 (2003)

    Article  Google Scholar 

  • King, P., Tester, J.: The landscape of persuasive technologies. Commun. ACM 42(5), 31–38 (1999)

    Article  Google Scholar 

  • Bental, D., Cawsey, A.: Personalized and adaptive systems for medical consumer applications. Commun. ACM 45, 62–63 (2002)

    Article  Google Scholar 

  • Healthcare satisfaction study 2000: Harris Interactive/ARiA marketing, World Wide Web (2000), http://www.harrisinteractive.com/news/downloads/HarrisAriaHCSatRpt.pdf

  • Abrams, D.B., Mills, S., Bulger, D.: Challenges and future directions for tailored communication research. Ann. Behav. Med. Publ. Soc. Behav. Med. 21(4), 299 (1999)

    Article  Google Scholar 

  • Rakowski, W., Andersen, M.R., Stoddard, A.M., et al.: Confirmatory analysis of opinions regarding the pros and cons of mammography. Health Psychol. Off. J. Div. Health Psychol. Am. Psychol. Assoc. 16(5), 433 (1997)

    Google Scholar 

  • Revere, D., Dunbar, P.J.: Review of computer-generated outpatient health behavior interventions: clinical encounters “in absentia”. J. Am. Med. Inform. Assoc. 8(1), 62–79 (2011)

    Article  Google Scholar 

  • Ryan, P., Lauver, D.R.: The efficacy of tailored interventions. J. Nurs. Scholarsh. 34(4), 331–337 (2002)

    Article  Google Scholar 

  • De Vries, H., Brug, J.: Computer-tailored interventions motivating people to adopt health promoting behaviours: introduction to a new approach. Patient Educ. Couns. 36(2), 99 (1999)

    Article  Google Scholar 

  • Kreuter, M.W., Skinner, C.S.: Tailoring: what’s in a name? Health Educ. Res. 15(1), 1 (2000)

    Article  Google Scholar 

  • Velicer, W.F., Diclemente, C.C.: Decisional balance measure for assessing and predicting smoking status. J. Pers. Soc. Psychol. 48(5), 1279–1289 (1985)

    Article  Google Scholar 

  • Janis, I.L., Mann, L.: Decision making: a psychological analysis of conflict, choice, and commitment. Am. Polit. Sci. Assoc. 73(1) (1977)

    Google Scholar 

  • Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Adv. Behav. Res. Ther. 1(4), 139–161 (1977)

    Article  Google Scholar 

  • Bandura, A.: Self-Efficacy Mechanism in Human Agency. Am. Psychol. 37(2), 122–147 (1982)

    Article  Google Scholar 

  • O’Keefe, R.M., Mceachern, T.: Web-based customer decision support systems. Commun. ACM 41(3), 71–78 (1998)

    Article  Google Scholar 

  • Culnan, M.J.: Chauffeured versus end user access to commerical databases: the effects of task and individual differences. MIS Q. 7(1), 55–67 (1983)

    Article  Google Scholar 

  • Wilson, E.V.: Asynchronous health care communication. Commun. ACM 46(6), 79–84 (2003)

    Article  Google Scholar 

  • Friedman, R.H., Stollerman, J.E., Mahoney, D.M., et al.: The virtual visit: using telecommunications technology to take care of patients. J. Am. Med. Inform. Assoc. 4(6), 413 (1997)

    Article  Google Scholar 

  • Guinea, A.O.D., Titah, R., Léger, P.-M.: Explicit and implicit antecedents of users’ behavioral beliefs in information systems: a neuro psychological investigation. J. Manag. Inf. Syst. 30(4), 179–210 (2014)

    Article  Google Scholar 

  • Paul, D.L.: Collaborative activities in virtual settings: a knowledge management perspective of telemedicine. J. Manag. Inf. Syst. 22(4), 143–176 (2006)

    Article  Google Scholar 

  • Xu, D.J., Liao, S.S., Li, Q.: Combining empirical experimentation and modeling techniques: a design research approach for personalized mobile advertising applications. Decis. Support Syst. 44(3), 710–724 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Management, eHealth Research Institute, Harbin Institute of Technology, Harbin, China

    Shuqing Chen, Xitong Guo & Xiaofeng Ju

Authors
  1. Shuqing Chen
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  2. Xitong Guo
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  3. Xiaofeng Ju
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Corresponding author

Correspondence to Shuqing Chen .

Editor information

Editors and Affiliations

  1. University of Arizona, Tucson, AZ, USA

    Hsinchun Chen

  2. Wuhan University, Wuhan, China

    Qing Fang

  3. University of Arizona, Tucson, AZ, USA

    Daniel Zeng

  4. Wuhan University, Wuhan, China

    Jiang Wu

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Chen, S., Guo, X., Ju, X. (2018). The Design of Personalized Artificial Intelligence Diagnosis and the Treatment of Health Management Systems Simulating the Role of General Practitioners. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds) Smart Health. ICSH 2018. Lecture Notes in Computer Science(), vol 10983. Springer, Cham. https://doi.org/10.1007/978-3-030-03649-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-03649-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03648-5

  • Online ISBN: 978-3-030-03649-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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