Migraine Generative Artificial Intelligence based on Mobile Personalized Healthcare
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Abstract
Migraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A migraine attack induces intense pain, hindering an individual from engaging in daily activities and potentially persisting for hours or even days. By the growth of the Internet of Things, we have new opportunities to try to apply it to the medical field. To identify the origin of a migraine, specialists need access to a patient's medical history and a comprehensive understanding of migraine symptoms for effective treatment. Determining the true source of a migraine may take longer than expected. Nowadays, solving problems through the Internet has become very common in people's lives. Hence, the objective of this research is to create a mobile personalized healthcare mechanism that can assist migraine patients in promptly receiving optimal and precise treatment. Moreover, this research would establish a user-friendly interface that facilitates the presentation of compelling evidence regarding the repercussions of patient health issues. Additionally, machine learning training was designed to treat patients based on relevant demographic characteristics of the healthcare treatment, such as medical history and reports provided. Therefore, this paper can provide insights into the state of art in mobile based personalized healthcare system to recommend future paths, for integration and investigation to improve online migraine platforms for a wide range of migraine patients.
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References
G., Tsaramirsis, A., Kantaros, I., Al-Darraji, D., Piromalis, C., Apostolopoulos, A., Pavlopoulou, M., Alrammal, Z., Ismail, S. M., Buhari, M., Stojmenovic, & H., Tamimi, “A modern approach towards an industry 4.0 model: From driving technologies to management”, Journal of Sensors, 2022 (1), p. 5023011, DOI: https://doi.org/10.1155/2022/5023011
T. C. L., Chow, & W. W., Ma, “A Longitudinal Study on Smartphone Use in Hong Kong”, In New Media for Educational Change: Selected Papers from HKAECT 2018 International Conference, pp. 203-216, 2018, Springer Singapore, DOI: https://doi.org/10.1007/978-981-10-8896-4_17
M. A., Pescador Ruschel, & O., De Jesus, (2020). Migraine Headache. StatPearls. NIH (National Institutes of Health), National Library of Medicine. Retrieved December 12, 2024, from https://www.ncbi.nlm.nih.gov/books/NBK560787
D., Kim, S., Oh, & T., Shon, “Digital forensic approaches for metaverse ecosystems”, Forensic Science International: Digital Investigation, 46, p. 301608, 2023, DOI: https://doi.org/10.1016/j.fsidi.2023.301608
R., Yang, T. F., Tan, W., Lu, A. J., Thirunavukarasu, D. S. W., Ting, & N., Liu, “Large language models in health care: Development, applications, and challenges”, Health Care Science, 2(4), pp.255-263, 2023, DOI: https://doi.org/10.1002/hcs2.61
P., Zhang, & M. N., Kamel Boulos, “Generative AI in medicine and healthcare: promises, opportunities and challenges”, Future Internet, 15(9), p. 286, 2023, DOI: https://doi.org/10.3390/fi15090286
B., Meskó, & E. J., Topol, “The imperative for regulatory oversight of large language models (or generative AI) in healthcare”, NPJ digital medicine, 6(1), p. 120, 2023, DOI: https://doi.org/10.1038/s41746-023-00873-0
H., Soumare, A., Benkahla, & N., Gmati, “Deep learning regularization techniques to genomics data”, Array, 11, p.100068, 2021, DOI: https://doi.org/10.1016/j.array.2021.100068
Y., Tian, & Y., Zhang, “A comprehensive survey on regularization strategies in machine learning”, Information Fusion, 80, pp. 146-166, 2022, DOI: https://doi.org/10.1016/j.inffus.2021.11.005
R., Moradi, R., Berangi, & B., Minaei, “A survey of regularization strategies for deep models”, Artificial Intelligence Review, 53(6), pp. 3947-3986, 2020, DOI: https://doi.org/10.1007/s10462-019-09784-7
D. A., Clevert, T., Unterthiner, G., Povysil, & S., Hochreiter, “Rectified factor networks for biclustering of omics data”, Bioinformatics, 33(14), pp. i59-i66, 2017, DOI: https://doi.org/10.1093/bioinformatics/btx226
A., Urooj, & A., Nasir, “Neural network-based self-tuning control for hybrid electric vehicle engines”, Engineering Applications of Artificial Intelligence, 138, p. 109275, 2024, DOI: https://doi.org/10.1016/j.engappai.2024.109275
B. F., Wee, S., Sivakumar, K.H., Lim, W. K., Wong, & F. H., Juwono, “Diabetes detection based on machine learning and deep learning approaches”, Multimedia Tools and Applications, 83(8), pp. 24153-24185, 2024, DOI: https://doi.org/10.1007/s11042-023-16407-5
A., Apicella, F., Donnarumma, F., Isgrò, & R., Prevete, “A survey on modern trainable activation functions. Neural Networks”, 138, pp. 14-32, 2021, DOI: https://doi.org/10.1016/j.neunet.2021.01.026
J., Yang, H., Jin, R., Tang, X., Han, Q., Feng, H., Jiang, S., Zhong, B., Yin, & X., Hu, “Harnessing the power of llms in practice: A survey on chatgpt and beyond”, ACM Transactions on Knowledge Discovery from Data, 18(6), pp. 1-32, 2024, DOI: https://doi.org/10.1145/3649506
J. L., Ba, J. R., Kiros, & G. E., Hinton, (2016). Layer normalization. arXiv: 1607.06450. Retrieved December 12, 2024, from https://arxiv.org/abs/1607.06450
A., Ziaee, & E., Çano, “Batch Layer Normalization A new normalization layer for CNNs and RNNs”, In Proceedings of the 6th International Conference on Advances in Artificial Intelligence (pp. 40-49), 2022, October, DOI: https://doi.org/10.1145/3571560.3571566
S., Naseem, “Advancing Health Literacy Through Generative AI: The Utilization of Open-Source Large Language Models (LLMS) for Text Simplification and Readability”, Master thesis, Michigan Technological University, 2024, DOI: https://doi.org/10.37099/mtu.dc.etdr/1762
A., Vaswani, N., Shazeer, N., Parmar, J., Uszkoreit, L., Jones, A. N., Gomez, L., Kaiser, & I., Polosukhin, (2017). Attention is All you Need. Advances in Neural Information Processing Systems, arXiv (Cornell University), 30, pp. 5998–6008. Retrieved December 12, 2024, from https://arxiv.org/pdf/1706.03762v5
B., Lutkevich, & E., Burns, (2021). Natural language processing (NLP). TechTarget: Newton, MA, USA. Retrieved December 12, 2024, from https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP
T., Webb, K. J., Holyoak, & H., Lu, “Emergent analogical reasoning in large language models”, Nature Human Behaviour, 7(9), pp. 1526-1541, 2023, DOI: https://doi.org/10.1038/s41562-023-01659-w
W., Zhou, H., Wu, J., Xu, M., Zeineldeen, C., Luscher, R., Schluter, & H., Ney, “Enhancing and adversarial: Improve ASR with speaker labels”, In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5), IEEE, 2023, June, DOI: https://doi.org/10.1109/ICASSP49357.2023.10096722
Y., Yang, S., Xinyang, Q., Wang, & C., Fang, “Enhancement of electromagnetic scattering computation acceleration using LSTM neural networks”, Electronics, 12(18), p. 3900, 2023, DOI: https://doi.org/10.3390/electronics12183900
C., Yin, K., Du, Q., Nong, H., Zhang, L., Yang, B., Yan, X., Huang, X., Wang, & X., Zhang, “PowerPulse: Power energy chat model with LLaMA model fine-tuned on Chinese and power sector domain knowledge”, Expert Systems, 41(3), p.e13513, 2024, DOI: https://doi.org/10.1111/exsy.13513
S.-B., Ho, E.-Y., Chew, & C.-H., Tan, “Streamlining Dental Clinic Management for Effective Digitisation Productivity and Usability”, Journal of Informatics and Web Engineering (JIWE), 3(2), pp. 70-85, 2024, DOI: https://doi.org/10.33093/jiwe.2023.3.2.5
J.-L., Goh, S.-B., Ho, & C.-H., Tan, “Weather-Based Arthritis Tracking: A Mobile Mechanism for Preventive Strategies”, Journal of Informatics and Web Engineering (JIWE), 3(1), pp. 210-225, 2024, DOI: https://doi.org/10.33093/jiwe.2024.3.1.14
S. N., Kalid, S.-B., Ho, A., Ibrahim, J. H. N., Azman, S.-J., Tan, & J.-E., Cheong, “Enhancing personalized healthcare with an effective E-Healthcare Management System”, Journal of System and Management Sciences (JSMS), 14(2), pp. 482–497, 2024, DOI: https://doi.org/10.33168/JSMS.2024.0230
X.-X., Cho, S.-B., Ho, & C.-H., Tan, “Improving asthma treatment adherence by integrating weather information with responsive web technique”, AIP Conference Proceedings, vol. 3153, 030005, 2024, DOI: https://doi.org/10.1063/5.0216655
G. A., Makho, S.-B., Ho, & I., Chai, “Exploring the potential of artificial intelligence in the medical sector for patient well-being: A review technique”, AIP Conference Proceedings, vol. 3153, 030003, 2024, DOI: https://doi.org/10.1063/5.0216651
R. J. Y., Kok, S.-B., Ho, & C.-H., Tan, “Improving the prediction resolution time for mobile eczema support system”, AIP Conference Proceedings, vol. 3153, 030007, 2024, DOI: https://doi.org/10.1063/5.0216658
J., Jayapradha, Y., Kulkarni, P., Naveen, & E.A., Anaam, “Treatment recommendation using BERT personalization”, Journal of Informatics and Web Engineering (JIWE), 3(3), pp. 41-62, 2024, DOI: https://doi.org/10.33093/jiwe.2024.3.3.3
C. M., Chituru, S.-B., Ho, & I., Chai, “Integrating spatial computing with clinical pathology for enhanced diagnosis and treatment informatics in healthcare”, International Journal on Informatics Visualization (JOIV), 8(3-2), pp. 1762-1771, 2024, DOI: https://doi.org/10.62527/joiv.8.3-2.2951
I. Ibriwesh, S.-B. Ho, I. Chai, & C. H. Tan, “A controlled experiment on comparison of data perspectives for software requirements documentation,” Arabian Journal for Science and Engineering, 42, pp. 3175-3189, 2017, DOI: https://doi.org/10.1007/s13369-017-2425-2
S.-B. Ho, I. Chai, & C. H. Tan, “An empirical investigation of methods for teaching design patterns within object-oriented frameworks,” International Journal of Information Technology & Decision Making, 6(4), pp. 701-722, 2007, DOI: https://doi.org/10.1142/S021962200700271X
W.-X., Ong, S.-B., Ho, & C.-H., Tan, “Enhancing Migraine Management System through Weather Forecasting for a Better Daily Life”, Journal of Informatics and Web Engineering (JIWE), 2(2), pp. 201-217, 2023, DOI: https://doi.org/10.33093/jiwe.2023.2.2.15
S.-B. Ho, S.-L Chean, I. Chai, & C.-H. Tan, “Engineering meaningful computing education: programming learning experience model,” In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), (pp. 925-929), IEEE, 2019. DOI: https://doi.org/10.1109/IEEM44572.2019.8978920
S.-B. Ho, I. Chai, & C. H. Tan, “Leveraging framework documentation solutions for intermediate users in knowledge acquisition,” International Journal of Information Science, 3(1), pp. 13-23, 2013
R. Haque, S.-B. Ho, I. Chai, C.-W. Teoh, A. Abdullah, C.-H. Tan, & K. S. Dollmat, “Intelligent health informatics with personalisation in weather-based healthcare using machine learning,” In International Conference of Reliable Information and Communication Technology (pp. 29–40), Springer International Publishing, Cham, 2020, December, DOI: https://doi.org/10.1007/978-3-030-70713-2_4
C.-W. Teoh, S.-B. Ho, K. S. Dollmat, & I. Chai, “An evolutionary algorithm-based optimization ensemble learning model for predicting academic performance,” In Proceedings of the 2022 11th International Conference on Software and Computer Applications (pp. 102-107), ACM, 2022, February, DOI: https://doi.org/10.1145/3524304.3524320