Artificial Intelligence, Diagnostic and Prevention Tools to Deal with Covid 19 In Algeria
DOI:
https://doi.org/10.52783/jns.v14.2621Keywords:
Medical self-diagnosis, COVID-19 disease, CNN, drug consumption, prediction systemAbstract
In this work, we suggest an original architecture for medical self-diagnosis. Two intelligent models have been created to interact with patients and suggest appropriate medical treatments. This architecture enhances the obtained results through the integration of a Chatbot and an image classification system. The originality of our work lies in the design and development of novel diagnostic procedures. Another unique aspect of our study is the introduction of an architecture that combines text and image classification to identify suitable medications. To validate our proposal, we used COVID-19 disease as a case study, enabling us to guide patients with the assistance of an intelligent agent. Our model was trained using Convolutional Neural Networks (CNNs) on a dataset comprising three classes: COVID, normal, and viral pneumonia. We introduced a new stopping criterion based on accuracy, effectively reducing learning time and preventing confusion. In the latter part of this paper, we examined the consumption patterns of anticoagulants, antibiotics, anti-inflammatories, and analgesics over two years: pre-COVID-19 (2018-2019) and through COVID-19 (2020-2021). Our dataset was compiled from data collected at Mohamed Boudiaf Barika Hospital in Algeria. Based on this study, we proposed a predictive system for drug consumption trends.
Downloads
Metrics
References
Amer, Eslam, Ahmed Hazem, Omar Farouk, Albert Louca, Youssef Mohamed, and Michel Ashraf. (2021). “A Proposed Chatbot Framework for COVID-19.” In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 263–68. IEEE. https://doi.org/10.1109/MIUCC52538.2021.9447652.
Anki, Prasnurzaki, Alhadi Bustamam, and Rinaldi Anwar Buyung. (2021). “Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories.” Electronics 10 (21): 2696. https://doi.org/10.3390/electronics10212696.
Atkinson, Anthony C., Marco Riani, and Aldo Corbellini. (2021). “The Box–Cox Transformation: Review and Extensions.” Statistical Science 36 (2). https://doi.org/10.1214/20-STS778.
Berghout, Tarek, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, and Mohamed Benbouzid. (2020). “Aircraft Engines Remaining Useful Life Prediction with an Adaptive Denoising Online Sequential Extreme Learning Machine.” Engineering Applications of Artificial Intelligence 96 (November): 103936. https://doi.org/10.1016/j.engappai.2020.103936.
Chebout, Mohamed Sedik, and Oussama Kabour. (2022). “Machine Learning-Based Forecasting Models for COVID-19 Spread in Algeria.” Model Assisted Statistics and Applications 17 (2): 99–108. https://doi.org/10.3233/MAS-220013.
Cheng, Xiaochun, Seifedine Kadry, Maytham N. Meqdad, and Rubén González Crespo. (2022). “CNN Supported Framework for Automatic Extraction and Evaluation of Dermoscopy Images.” The Journal of Supercomputing 78 (15): 17114–31. https://doi.org/10.1007/s11227-022-04561-w.
Cui, Fuwei, Hui Di, Hui Huang, Kazushige Ouchi, Ze Liu, and Jinan Xu. (2022). “Hierarchical Latent Variables Structure for Topic Aware Multi-Turn Conversation.” Journal of Intelligent & Fuzzy Systems 43 (3): 3805–14. https://doi.org/10.3233/JIFS-211886.
Dhyani, Manyu, and Rajiv Kumar. (2021). “An Intelligent Chatbot Using Deep Learning with Bidirectional RNN and Attention Model.” Materials Today: Proceedings 34: 817–24. https://doi.org/10.1016/j.matpr.2020.05.450.
Fan, Xiangmin, Daren Chao, Zhan Zhang, Dakuo Wang, Xiaohua Li, and Feng Tian. (2021). “Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study.” Journal of Medical Internet Research 23 (1): e19928. https://doi.org/10.2196/19928.
Fasnacht, Laurent. (2018). “Mmappickle: Python 3 Module to Store Memory-Mapped Numpy Array in Pickle Format.” Journal of Open Source Software 3 (26): 651. https://doi.org/10.21105/joss.00651.
Guo, Hongming, and Xiaochun Cheng. (2018). “Individual Recommendation Method of College Physical Education Resources Based on Cognitive Diagnosis Model.” ICST Transactions on Scalable Information Systems, July, 173379. https://doi.org/10.4108/eai.10-2-2022.173379.
Hasan, Rajibul, Bernadett Koles, Mustafeed Zaman, and Justin Paul. (2021). “The Potential of Chatbots in Travel and Tourism Services in the Context of Social Distancing.” International Journal of Technology Intelligence and Planning 13 (1): 63. https://doi.org/10.1504/IJTIP.2021.117998.
Hsueh, Yu-Ling, and Tai-Liang Chou. (2023). “A Task-Oriented Chatbot Based on LSTM and Reinforcement Learning.” ACM Transactions on Asian and Low-Resource Language Information Processing 22 (1): 1–27. https://doi.org/10.1145/3529649.
Mehfooz, Fahad, Sakshi Jha, Sahil Singh, Shreya Saini, and Nidhi Sharma. (2021). “Medical Chatbot for Novel COVID-19.” In , 423–30. https://doi.org/10.1007/978-981-15-8354-4_42.
Mellia, Joseph A., Marten N. Basta, Yoshiko Toyoda, Sammy Othman, Omar Elfanagely, Martin P. Morris, Luke Torre-Healy, Lyle H. Ungar, and John P. Fischer. (2021). “Natural Language Processing in Surgery.” Annals of Surgery 273 (5): 900–908. https://doi.org/10.1097/SLA.0000000000004419.
Merahi, Fateh, and Abdelouahab Bibi. (2021). “Evolutionary Transfer Functions Solution for Continuous–Time Bilinear Stochastic Processes with Time-Varying Coefficients.” Communications in Statistics - Theory and Methods 50 (22): 5189–5214. https://doi.org/10.1080/03610926.2020.1726390.
Mittal, Mamta, Gopi Battineni, Dharmendra Singh, Thakursingh Nagarwal, and Prabhakar Yadav. (2021). “Web-Based Chatbot for Frequently Asked Queries (FAQ) in Hospitals.” Journal of Taibah University Medical Sciences 16 (5): 740–46. https://doi.org/10.1016/j.jtumed.2021.06.002.
Mutiwokuziva, Milla T, Melody W Chanda, Prudence Kadebu, Addlight Mukwazvure, and Tatenda T Gotora. (2017). “A Neural-Network Based Chat Bot.” In 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 212–17. IEEE. https://doi.org/10.1109/CESYS.2017.8321268.
Ni, Jiasheng. (2021). “A Medical Service Application Based on 3D-CNN and Knowledge Graph.” Journal of Physics: Conference Series 2078 (1): 012048. https://doi.org/10.1088/1742-6596/2078/1/012048.
Sharma, Vibhor, Shashi Bhushan, Anuj Kumar Singh, and Pramod Kumar. (2022). “A Novel Secure Vector Product for Protecting the Privacy of Data in Vertically Partitioned Dataset.” In , 285–95. https://doi.org/10.1007/978-981-19-2828-4_28.
Shum, Heung-yeung, Xiao-dong He, and Di Li. (2018). “From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots.” Frontiers of Information Technology & Electronic Engineering 19 (1): 10–26. https://doi.org/10.1631/FITEE.1700826.
Tang, Yuan. (2016). “TF.Learn: TensorFlow’s High-Level Module for Distributed Machine Learning,” December. http://arxiv.org/abs/1612.04251.
Temniranrat, Pitchayagan, Kantip Kiratiratanapruk, Apichon Kitvimonrat, Wasin Sinthupinyo, and Sujin Patarapuwadol. (2021). “A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot.” Computers and Electronics in Agriculture 185 (June): 106156. https://doi.org/10.1016/j.compag.2021.106156.
Tsuru, Satoko, Tetsuro Tamamoto, Akihiro Nakao, Kouichi Tanizaki, and Naohisa Yahagi. (2022). “Patient Data Sharing and Reduction of Overtime Work of Nurses by Innovation of Nursing Records Using Structured Clinical Knowledge.” In . https://doi.org/10.3233/SHTI220514.
Urquiza-Yllescas, José Fidel, Sonia Mendoza, José Rodríguez, and Luis Martín Sánchez-Adame. (2022). “An Approach to the Classification of Educational Chatbots.” Journal of Intelligent and Fuzzy Systems 43 (4): 5095–5107. https://doi.org/10.3233/JIFS-213275.
Wang, Shui-Hua, Yin Zhang, Xiaochun Cheng, Xin Zhang, and Yu-Dong Zhang. (2021). “PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.” Edited by Martti Juhola. Computational and Mathematical Methods in Medicine 2021 (March): 1–18. https://doi.org/10.1155/2021/6633755.
Xu, Ruijian, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, and Dongyan Zhao. (2021). “Response Ranking with Multi-Types of Deep Interactive Representations in Retrieval-Based Dialogues.” ACM Transactions on Information Systems 39 (4): 1–28. https://doi.org/10.1145/3462207.
Zhang, Zhe, Yiyang Zhang, Xiang Li, Yurong Qian, and Tao Zhang. (2022). “BMCSA:Multi-Feature Spatial Convolution Semantic Matching Model Based on BERT.” Journal of Intelligent & Fuzzy Systems 43 (4): 4083–93. https://doi.org/10.3233/JIFS-212624.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
