Patients' treatments are becoming more personalized as healthcare becomes more commodified. Meeting this need requires not just a large allocation of capital, but also a comprehensive application of information, resulting in efforts like electronic health record standards. The quantity of medical data accessible for analytics and data extraction will grow rapidly as these become more mainstream. This is accompanied by an increase in new methods for non-invasive assessment and collection of medically important data in different forms, such as signals and pictures. Despite problems with standardisation and availability, the enormous quantity of data that results is a significant tool for the machine learning industry. Biomedical Computational Intelligence (CI) technologies are already flourishing as a result of getting into this data stream. The legislative session "Computer science and information Intelligence in Biology and medicine" at European Symposium on Artificial Neural Networks (ESANN) addresses some of the field's most pressing issues. This paper introduces the theme session by highlighting a few of the submissions and pointing out possibilities and difficulties for CI in biomedicine.
Keywords
Web-Based Medical Services (WBMS), Computational Intelligence (CI), Perceived Ease of Use (PEOU), Perceived Utility (PU)
I. Puspitasari and V. Briliana, “Pengaruh perceived ease-of-use, perceived usefulness, trust Dan perceived enjoyment terhadap repurchase intention (studi kasus Pada website Zalora Indonesia),” Jurnal Bisnis dan Akuntansi, vol. 19, no. 2, pp. 171–182, 2018.
R. Cardwell, L. McKenna, J. Davis, and R. Gray, “How is clinical credibility defined in nursing? A concept mapping study,” J. Clin. Nurs., vol. 30, no. 17–18, pp. 2441–2452, 2021.
V. T. Binh and D. T. N. Huy, “Further analysis on solution treatment for diabetes of patients at hospitals in Vietnam,” Neuroquantology, vol. 19, no. 8, pp. 88–93, 2021.
T. J. Brigham, “Taking advantage of Google’s Web-based applications and services,” Med. Ref. Serv. Q., vol. 33, no. 2, pp. 202–210, 2014.
S. Omboni, L. Campolo, and E. Panzeri, “Telehealth in chronic disease management and the role of the Internet-of-Medical-Things: the Tholomeus® experience,” Expert Rev. Med. Devices, vol. 17, no. 7, pp. 659–670, 2020.
T.-H. Hsu, Y.-S. Wang, and S.-C. Wen, “Using the decomposed theory of planning behavioural to analyse consumer behavioural intention towards mobile text message coupons,” J. Target. Meas. Anal. Mark., vol. 14, no. 4, pp. 309–324, 2006.
H. Li, S. Wang, J. Tang, J. Wu, and Y. Liu, “Computed tomography- (CT-) based virtual surgery planning for spinal intervertebral foraminal assisted clinical treatment,” J. Healthc. Eng., vol. 2021, p. 5521916, 2021.
R. Koenraadt and K. van de Ven, “The Internet and lifestyle drugs: an analysis of demographic characteristics, methods, and motives of online purchasers of illicit lifestyle drugs in the Netherlands,” Drugs (Abingdon Engl.), vol. 25, no. 4, pp. 345–355, 2018.
P. M. dos Santos and M. Â. Cirillo, “Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions,” Commun. Stat. Simul. Comput., pp. 1–13, 2021.
A. Javaid, M. S. Nazir, and K. Fatima, “Impact of corporate governance on capital structure: mediating role of cost of capital,” J. Econ. Adm. Sci., vol. ahead-of-print, no. ahead-of-print, 2021.
J. Slostad, A. Masood, A. T. Swoboda, and M. A. Levy, “Towards the clinical validity of tumor organoid drug screens: Establishing a framework for organoid disease models,” J. Clin. Oncol., vol. 39, no. 15_suppl, pp. e15037–e15037, 2021.
N. M. Hudak, S. O. Pinheiro, and M. Yanamadala, “Increasing physician assistant students’ team communication skills and confidence throughout clinical training,” J. Physician Assist. Educ., vol. 30, no. 4, pp. 219–222, 2019.
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Wang Dong
School of Design, University of Washington, Seattle, WA.
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Wang Dong, “Technological Effectiveness, Clinical Credibility, Data Sources, and WBMS Behavioural Intention”, Journal of Biomedical and Sustainable Healthcare Applications, vol.2, no.1, pp. 059-066, January 2022. doi: 10.53759/0088/JBSHA202202008.
