Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients
In the field of biomedical engineering, the issue of drug delivery constitutes a multifaceted and demanding endeavor for healthcare professionals. The intravenous administration of pharmacological agents to patients and the normalization of average arterial blood pressure (AABP) to desired threshold...
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Mai The Vu Seong Han Kim Ha Le Nhu Ngoc Thanh Majid Roohi Tuan Hai Nguyen |
| author_facet | Mai The Vu Seong Han Kim Ha Le Nhu Ngoc Thanh Majid Roohi Tuan Hai Nguyen |
| author_sort | Mai The Vu |
| collection | DOAJ |
| description | In the field of biomedical engineering, the issue of drug delivery constitutes a multifaceted and demanding endeavor for healthcare professionals. The intravenous administration of pharmacological agents to patients and the normalization of average arterial blood pressure (AABP) to desired thresholds represents a prevalent approach employed within clinical settings. The automated closed-loop infusion of vasoactive drugs for the purpose of modulating blood pressure (BP) in patients suffering from acute hypertension has been the focus of rigorous investigation in recent years. In previous works where model-based and fuzzy controllers are used to control AABP, model-based controllers rely on the precise mathematical model, while fuzzy controllers entail complexity due to rule sets. To overcome these challenges, this paper presents an adaptive closed-loop drug delivery system to control AABP by adjusting the infusion rate, as well as a communication time delay (CTD) for analyzing the wireless connectivity and interruption in transferring feedback data as a new insight. Firstly, a nonlinear backstepping controller (NBC) is developed to control AABP by continuously adjusting vasoactive drugs using real-time feedback. Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. Besides the various analyses such as normal condition (without CTD strategy), stability, and hybrid noise, a CTD analysis is implemented to illustrate the functionality of the system in a wireless manner and interruption in real-time feedback data. |
| format | Article |
| id | doaj-art-580b4212870e4b44a9a9b6f1d7d8f1bf |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-580b4212870e4b44a9a9b6f1d7d8f1bf2025-01-10T13:18:21ZengMDPI AGMathematics2227-73902025-01-0113113610.3390/math13010136Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive PatientsMai The Vu0Seong Han Kim1Ha Le Nhu Ngoc Thanh2Majid Roohi3Tuan Hai Nguyen4Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of KoreaDepartment of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of KoreaFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 71307, VietnamDepartment of Mathematics, Aarhus University, 8000 Aarhus, DenmarkFaculty of Engineering, Dong Nai Technology University, Bien Hoa City, VietnamIn the field of biomedical engineering, the issue of drug delivery constitutes a multifaceted and demanding endeavor for healthcare professionals. The intravenous administration of pharmacological agents to patients and the normalization of average arterial blood pressure (AABP) to desired thresholds represents a prevalent approach employed within clinical settings. The automated closed-loop infusion of vasoactive drugs for the purpose of modulating blood pressure (BP) in patients suffering from acute hypertension has been the focus of rigorous investigation in recent years. In previous works where model-based and fuzzy controllers are used to control AABP, model-based controllers rely on the precise mathematical model, while fuzzy controllers entail complexity due to rule sets. To overcome these challenges, this paper presents an adaptive closed-loop drug delivery system to control AABP by adjusting the infusion rate, as well as a communication time delay (CTD) for analyzing the wireless connectivity and interruption in transferring feedback data as a new insight. Firstly, a nonlinear backstepping controller (NBC) is developed to control AABP by continuously adjusting vasoactive drugs using real-time feedback. Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. Besides the various analyses such as normal condition (without CTD strategy), stability, and hybrid noise, a CTD analysis is implemented to illustrate the functionality of the system in a wireless manner and interruption in real-time feedback data.https://www.mdpi.com/2227-7390/13/1/136average arterial blood pressure (AABP)vasoactive drugsnonlinear backstepping controller (NBC)model-free reinforcement learning (MF-DRL)communication time delay (CTD) |
| spellingShingle | Mai The Vu Seong Han Kim Ha Le Nhu Ngoc Thanh Majid Roohi Tuan Hai Nguyen Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients Mathematics average arterial blood pressure (AABP) vasoactive drugs nonlinear backstepping controller (NBC) model-free reinforcement learning (MF-DRL) communication time delay (CTD) |
| title | Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients |
| title_full | Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients |
| title_fullStr | Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients |
| title_full_unstemmed | Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients |
| title_short | Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients |
| title_sort | trust region policy learning for adaptive drug infusion with communication networks in hypertensive patients |
| topic | average arterial blood pressure (AABP) vasoactive drugs nonlinear backstepping controller (NBC) model-free reinforcement learning (MF-DRL) communication time delay (CTD) |
| url | https://www.mdpi.com/2227-7390/13/1/136 |
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