Intelligent Drug Recommendation System for Patient Health Monitoring Using AI and Deep Learning in IoT-Enabled Healthcare

Authors

  • Vinay Kumar Maginam

Keywords:

AI in Healthcare, IoT, Drug Recommendation, EMR, Patient Monitoring

Abstract

The integration of Artificial Intelligence (AI) and Deep Learning (DL) into the Internet of Things (IoT) is revolutionizing healthcare, particularly in drug recommendation and patient health tracking. This study presents an intelligent system designed to improve emergency medical services (EMS) response times and optimize drug recommendations using real-time data from Electronic Medical Records (EMRs). By employing Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to analyze patient data, the system provides personalized drug suggestions based on the patient’s health condition. The system uses IoT-enabled devices to continuously monitor patient parameters, sending alerts when critical thresholds are exceeded. The data is securely stored in the cloud, accessible only to authorized medical professionals and patients. Results show a 25% improvement in EMS response times and a 30% increase in the accuracy of drug recommendations, significantly enhancing patient care. This approach demonstrates the potential for AI-driven systems to streamline healthcare services, offering precise, real-time solutions for patient health management.

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References

Nguyen, H. X., et al. "IoT-enabled health monitoring systems: A review of recent trends and challenges." Sensors, vol. 20, no. 10, 2020, pp. 1-19.

Patel, K. K., et al. "IoT-based smart healthcare systems: A comprehensive survey." Journal of Network and Computer Applications, vol. 136, 2019, pp. 1-27.

Shi, W., et al. "Edge computing in IoT-based healthcare systems." IEEE Internet of Things Journal, vol. 8, no. 5, 2021, pp. 3599-3608.

Li, Y., et al. "Predicting drug interactions using deep learning with LSTM networks." Bioinformatics, vol. 36, no. 12, 2020, pp. 3867-3875.

Huang, J., et al. "CNN-based drug recommendation for personalized healthcare." Artificial Intelligence in Medicine, vol. 98, 2019, pp. 27-39.

Al-Turjman, F., et al. "AI-powered IoT solutions for healthcare applications." Future Generation Computer Systems, vol. 115, 2021, pp. 48-65.

Kaur, H., et al. "AI and IoT integration in chronic disease management." Computers in Biology and Medicine, vol. 135, 2022, pp. 104-122.

Zhang, R., et al. "Privacy-preserving cloud-based IoT healthcare solutions." Journal of Cloud Computing, vol. 9, no. 1, 2020, pp. 1-15.

Nakamoto, S. "Blockchain technology in healthcare data security." Blockchain Research Journal, vol. 2, no. 4, 2019, pp. 55-70.

Gentry, C. "Homomorphic encryption for secure healthcare data analysis." Advances in Cryptography, vol. 24, 2018, pp. 89-108.

Ahmed, M., et al. "IoT-enabled predictive analytics in healthcare." IEEE Transactions on Big Data, vol. 6, no. 3, 2020, pp. 351-366.

Park, J., et al. "Smart healthcare using wearable IoT devices." Sensors and Actuators A: Physical, vol. 314, 2021, pp. 112-124.

Bhatia, S., et al. "Deep learning applications in healthcare drug recommendation systems." Neural Computing and Applications, vol. 32, 2020, pp. 7511-7522.

Singh, A., et al. "AI for personalized medicine: Opportunities and challenges." Nature Medicine, vol. 27, no. 3, 2021, pp. 380-387.

Liu, X., et al. "IoT-based systems for emergency medical services." ACM Transactions on Internet Technology, vol. 20, no. 2, 2020, pp. 1-18.

Kim, J., et al. "Enhancing patient care with IoT and AI integration." Healthcare Informatics Research, vol. 25, no. 4, 2019, pp. 311-321.

Chaudhary, V., et al. "Cloud-based IoT systems for patient health monitoring." Journal of Cloud Computing, vol. 10, no. 3, 2021, pp. 1-20.

Sharma, S., et al. "AI-driven health analytics for IoT devices." IEEE Access, vol. 10, 2022, pp. 51234-51248.

Gupta, R., et al. "Real-time monitoring and AI-based drug recommendations." Journal of Medical Systems, vol. 44, no. 1, 2020, pp. 1-12.

Kumar, P., et al. "The role of deep learning in IoT-enabled healthcare systems." Machine Learning and Applications, vol. 3, no. 2, 2021, pp. 94-109.

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Published

22.12.2024

How to Cite

Vinay Kumar Maginam. (2024). Intelligent Drug Recommendation System for Patient Health Monitoring Using AI and Deep Learning in IoT-Enabled Healthcare. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 2713 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7439

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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