Multimodal data analysis for post-decortication therapy optimization using IoMT and reinforcement learning

Masood, Fahad and Ahmad, Jawad and Alasbali, Nada and Nafea, Ibtehal and Saeed, Faisal and Ullah, Rahmat (2025) Multimodal data analysis for post-decortication therapy optimization using IoMT and reinforcement learning. Journal of Intelligent Systems, 34 (1). ISSN 2191-026X

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Abstract

Multimodal neural network approaches mirror clinical decision-making processes for therapy optimization. Multiple data sources, including images, electronic health records, and the Internet of medical things, can be challenging for precise diagnoses. In this research, reinforcement learning, specifically deep Q-learning (DQL), utilizing multimodal data, has been employed to determine the most suitable treatment plans, particularly for patients undergoing lung decortication surgery. The model performance has been evaluated using rewards, epsilon decay, and Q -values across three different actions. The model’s performance has also been compared with machine learning models, such as Naïve Bayes, K-nearest neighbor, random forest, logistic regression, and support vector machine, regarding several performance metrics, including accuracy, precision, recall, and the area under the curve. Our findings demonstrate that the DQL model effectively learns optimal actions, significantly enhancing therapy optimization.

Item Type:	Article
Identification Number:	10.1515/jisys-2024-0417
Dates:	Date Event 27 June 2025 Accepted 8 October 2025 Published Online
Uncontrolled Keywords:	multimodal data, reinforcement learning, deep Q-learning, IoMT; EHR
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	09 Mar 2026 10:55
Last Modified:	09 Mar 2026 10:55
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16912

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