Abstract
The smart decision-making support framework is typically referred to as artificial intelligence (AI). The clinical decision framework can transform the process of decision-making using various technologies. These technologies incorporate framework engineering and information technology. The vital centered ontology-centered automatic reasoning which is incorporated in machine learning methodologies has been established in the present patient databases. The approach evaluated in this paper is in the support of the interoperability between various health information systems (HIS). This has been evaluated in sampling implementations that link up to three separate databases: drug prescriptions guidelines, drug-to-drug interaction and patient information which are databases used to showcase the efficiency of an algorithm used to provide effective healthcare decisions. Generally, the possibility of artificial intelligence was evaluated in the process of supporting tasks that are essential for medical experts including the aspect of coping up with noisy and missing patient information and enhancing the utility of various healthcare datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
C. Bennett, K. Hauser, Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artif. Intell. Med. 57(1), 9–19 (2013). https://doi.org/10.1016/j.artmed.2012.12.003
P. Lucas, Dealing with medical knowledge: Computers in clinical decision making. Artif. Intell. Med. 8(6), 579–580 (1996). https://doi.org/10.1016/s0933-3657(97)83108-9
W. Horn, Artificial intelligence in medicine and medical decision-making Europe. Artif. Intell. Med. 20(1), 1–3 (2000). https://doi.org/10.1016/s0933-3657(00)00049-x
P.L. Aaron, S. Bonni, An evaluation of wearable technological advancement in medical practices. J. Med. Image Comput. 58–65 (2020)
Web based analysis of critical medical care technology. J. Med. Image Comput. 66–73 (2020)
A. Haldorai, S. Anandakumar, Image segmentation and the projections of graphic centered approaches in medical image processing. J. Med. Image Comput. 74–81 (2020)
I. Rábová, V. Konečný, A. Matiášová, Decision making with support of artificial intelligence. Agric. Econ. (Zemědělská Ekonomika) 51(9), 385–388 (2012). https://doi.org/10.17221/5124-agricecon
R. Yager, Generalized regret based decision making. Eng. Appl. Artif. Intell. 65, 400–405 (2017). https://doi.org/10.1016/j.engappai.2017.08.001
N.M. Hewahi, A hybrid architecture for a decision making system. J. Artif. Intell. 2(2), 73–80 (2009). https://doi.org/10.3923/jai.2009.73.80
C. Gonzales, P. Perny, J. Dubus, Decision making with multiple objectives using GAI networks. Artif. Intell. 175(7–8), 1153–1179 (2011). https://doi.org/10.1016/j.artint.2010.11.020
Y. Chen, E. Ginell, An analysis of medical informatics and application of computer-aided decision support framework. J. Med. Image Comput. 10–17 (2020)
M. Heng Li, M. Yu Zhang, Computational benefits, limitations and techniques of parallel image processing. J. Med. Image Comput. 1–9 (2020)
R. Degani, G. Bortolan, Fuzzy decision-making in electrocardiography. Artif. Intell. Med. 1(2), 87–91 (1989). https://doi.org/10.1016/0933-3657(89)90020-1
P. Giang, P. Shenoy, Decision making on the sole basis of statistical likelihood. Artif. Intell. 165(2), 137–163 (2005). https://doi.org/10.1016/j.artint.2005.03.004
D. McSherry, Conversational case-based reasoning in medical decision making. Artif. Intell. Med. 52(2), 59–66 (2011). https://doi.org/10.1016/j.artmed.2011.04.007
T. Leong, Multiple perspective dynamic decision making. Artif. Intell. 105(1–2), 209–261 (1998). https://doi.org/10.1016/s0004-3702(98)00082-4
A. Кhusein, Clinical decision support system for the activity of evidence based computation. J. Med. Image Comput. 50–57 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Haldorai, A., Ramu, A. (2021). An Analysis of Artificial Intelligence Clinical Decision-Making and Patient-Centric Framework. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_62
Download citation
DOI: https://doi.org/10.1007/978-981-33-6862-0_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6861-3
Online ISBN: 978-981-33-6862-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)