A closed-loop healthcare processing approach based on deep reinforcement learning

Dai, Yinglong; Wang, Guojun; Muhammad, Khan; Liu, Shuai

doi:10.1007/s11042-020-08896-5

Cited by 11 publications

(19 citation statements)

References 33 publications

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“…Dai et al [111] simulated the human body using deep neural networks (DNNs) and utilized deep reinforcement learning (DRL) to find suitable treatment schemes for the simulated body. In this method, the simulated body plays the role of a patient and DRL plays the role of a physician.…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

“…DNN is a useful tool for modeling the human body because it has a global approximation capability. Therefore, in [111], DNN is used to simulate the human body.…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

“…Database. In [111], the authors use a database including 990 tongue images. These images include 9 different structures to train a deep neural network (DNN).…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

“…ML model development. The learning model presented in [111] includes two main components: simulated body and treatment part. The simulated body consists of two main parts, including regulating network and decoding network.…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

“…DNN is a useful tool for modeling the human body because it has a global approximation capability. Therefore, in [111], DNN is used to simulate the human body.…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

“…Database. In [111], the authors use a database including 990 tongue images. These images include 9 different structures to train a deep neural network (DNN).…”

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

Section: A Closed-loop Healthcare Processing Schemementioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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“…The basic system for different applications which are established in BAN is a wireless sensor network (WSN) [19]. The emerging wireless technologies are empowering due to their new, practical, and innovative design with various sensors for proper monitoring of the healthcare applications and other medical systems [20][21][22][23]. The main challenge is to decrease the consumption of energy during the working of the node which is that the actual data transmission will be doing.…”

Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

et al. 2021

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The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

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Skill Reward for Safe Deep Reinforcement Learning

Cheng

Zhang

et al. 2022

Communications in Computer and Information Science

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A closed-loop healthcare processing approach based on deep reinforcement learning

Cited by 11 publications

References 33 publications

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Skill Reward for Safe Deep Reinforcement Learning

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