A Study on Anaphylactic-shock Forecasting System with Knowledge-Based Intelligent Architecture
Keywords:
Anaphylaxis, Knowledge-Based system, Hybrid Architectures, Intelligent Agent, Machine LearningAbstract
A serious and potentially fatal allergic reaction Anaphylaxis requires prompt diagnosis and treatment. Accurate prediction of anaphylaxis is vital for preventing severe outcomes and accompanying medical management. This study investigates the creation and application of a hybrid knowledge-based system architecture to predict anaphylaxis. By combining rule-based systems with machine learning models, the goal is to integrate expert medical knowledge with data-driven insights, resulting in a robust, interpretable, and clinically useful predictive tool. Here the development of a hybrid Knowledge-Based Intelligent System Architecture is discussed. Also, this approach addresses a comprehensive study of the KBS-ML system’s capacity against traditional prediction methods, providing healthcare providers with timely and accurate identification of anaphylaxis risk, ultimately improving patient outcomes
References
Jawad, B.N., Shaker, S.M., Altintas, I., Eugen-Olsen, J., Nehlin, J.O., Andersen, O. and Kalle-mose, T. Development and validation of prognostic machine learning models for short-and long-term mor-tality among acutely admitted patients based on blood tests. Scientific Reports, 14(1), p.5942, 2024.
Birgit Obermeier, “Hybrid AI- Combined use of knowledge and data”, Plattform Lernende Systeme – Germany's Platform for Artificial Intelligence, Series: AI at a Glance, National Academy of Sci-ence and Engineering, 30 November 2023
Saxena, R.R., 2024. Examining Reactions about COVID-19 Vaccines: A Systematic Review of Studies Utilizing Deep Learning for Sentiment Analysis. Authorea Preprints
Riano, D., Real, F. and Alonso, J.R. “Improving resident's skills in the management of circulatory shock with a knowledge-based e-learning tool.”, International journal of medical informatics, 113, pp.49-55, 2018
Kierner, S., Kucharski, J. and Kierner, Z. “Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review.”, Journal of Bio-medical Informatics, p.104428, 2023
Segura-Bedmar, I. and Raez, P. “Cohort selection for clinical trials using deep learning models.”, Journal of the American Medical Informatics Association, 26(11), pp.1181-1188, 2019
Bal, M., Amasyali, M.F., Sever, H., Kose, G. and Demirhan, A. “Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.”, The Scientific World Journal, 2014
Srivastava, S.R. and Dubey, S. Knowledge-based systems in medical applications. In Computational intelligence and its applications in healthcare (pp. 189-215). Academic Press, 2020
Pulicharla, M.R., “Explainable AI in the Context of Data Engineering: Unveiling the Black Box in the Pipeline.”, International Journal of Innovative Science and Research Technology, Volume 9, ISSN No: -2456-2165, 2024
Amirahmadi, A., Ohlsson, M. and Etminani, K. “Deep learning prediction models based on EHR trajectories: A systematic review.”, Journal of biomedical informatics, p.104430, 2023
Sivasankari, S., Punnoose, D. and Krishnamoorthy, D. “A comparative study on the performance of rule engines in automated ontology learning: a case study with erythemato-squamous disease (ESD).”, International Journal of Intelligent Unmanned Systems, 8(4), pp.267-280, 2020
Habibi, L.N., Matsui, T. and Tanaka, T.S. “Critical evaluation of the effects of a cross-validation strategy and machine learning optimization on the prediction accuracy and transferability of a soy-bean yield prediction model using UAV-based remote sensing.”, Journal of Agriculture and Food Research, 16, p.101096, 2024
Farbiz, Farzam, "Knowledge-embedded machine learning and its applications in smart manu-fac-turing.", Journal of Intelligent Manufacturing 34.7 (2023): 2889-2906, 2023
Kierner, S., Kucharski, J. and Kierner, Z., “Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review.”, Journal of Bio-medical Informatics, p.104428, 2023
Mustaqeem, M., Mustajab, S. and Alam, M. “A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron.”, International Journal of Intelli-gent Computing and Cybernetics, 2024
Carrell, D.S., Gruber, S., Floyd, J.S., Bann, M., Cushing-Haugen, K., Johnson, R., Graham, V., Cronkite, D., Hazlehurst, B., Felcher, A.H. and Bejin, C.A., 2021. Improving methods of identifying anaphylaxis for medical product safety surveillance using natural language processing and machine learning. Pharmacoepidemiology and Drug Safety, 30, pp.16-17
Tavakolian, A., Rezaee, A., Hajati, F. and Uddin, S., 2023. Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model. Future Internet, 15(9), p.304
Bedraoui, A., Suntravat, M., El Mejjad, S., Enezari, S., Oukkache, N., Sanchez, E.E., Galan, J.A., El Fatimy, R. and Daouda, T., Medicine in Drug Discovery
Vats, S., Kukreja, V. and Mehta, S., 2024, March. A New Era in AgriTech: Federated Learning CNN for Jute Leaf Disease Identification. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1-6). IEEE
Gupta, J. and Seeja, K.R., 2024. A Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare. Archives of Computational Methods in Engineering, pp.1-26
Ahammed, M.F. and Labu, M.R., 2024. Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation. Journal of Medical and Health Studies, 5(2), pp.37-47
Lange, L., Wenzlitschke, N. and Rahm, E., 2024. Generating Synthetic Health Sensor Data for Pri-vacy-Preserving Wearable Stress Detection. arXiv preprint arXiv:2401.13327
Moulahoum, H. and Ghorbanizamani, F., 2024. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chemistry, p.138800
Borch, C., 2022. Machine learning, knowledge risk, and principal-agent problems in automated trading. Technology in society, 68, p.101852
Setiono, R., Mues, C. and Baesens, B., 2006. Risk management and regulatory compliance: A data mining framework based on neural network rule extraction. ICIS 2006 Proceedings, p.7
Kumar, R., Sangwan, K.S., Herrmann, C. and Ghosh, R., 2023. Development of a cyber physical production system framework for smart tool health management. Journal of Intelligent Manufac-turing, pp.1-30
Mazumdar, B.D. and Roy, S., 2021. Multi-Agent Paradigm for B2C E-Commerce. In Artificial Intel-ligence and Machine Learning in Business Management (pp. 29-52). CRC Press
Kharfan, M., Chan, V.W.K. and Firdolas Efendigil, T., 2021. A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches. Annals of Opera-tions Research, 303(1), pp.159-174
McNeil, C., Wong, P.F., Sridhar, N., Wang, Y., Santori, C., Wu, C.H., Homyk, A., Gutierrez, M., Beh-rooz, A., Tiniakos, D. and Burt, A.D., 2024. An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology. Modern Pathology, 37(2), p.100377
Borek, C., 2024. Comparative evaluation of llm-based approaches to chatbot creation
Khalid, M., 2024. Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Reviews, 51, p.101299
Morison, J. and Mclnerney, T., 2024. When should a computer decide? Judicial decision-making in the age of automation, algorithms and generative artificial intelligence. Research Handbook on Judging and the Judiciary
Ostropolets, A., 2023. Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias. Columbia University
Chevrier, R., Foufi, V., Gaudet-Blavignac, C., Robert, A. and Lovis, C., 2019. Use and understanding of anonymization and de-identification in the biomedical literature: scoping review. Journal of medical Internet research, 21(5), p.e13484
Rahmat, F., Zulkafli, Z., Ishak, A.J., Abdul Rahman, R.Z., Stercke, S.D., Buytaert, W., Tahir, W., Ab Rahman, J., Ibrahim, S. and Ismail, M., 2024. Supervised feature selection using principal component analysis. Knowledge and Information Systems, 66(3), pp.1955-1995.
