|
1
|
Rahman MZU, Akbar MA, Leiva V, Martin-Barreiro C, Imran M, Riaz MT, Castro C. An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients. Heliyon 2024; 10:e22454. [PMID: 38163138 PMCID: PMC10756970 DOI: 10.1016/j.heliyon.2023.e22454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024] Open
Abstract
In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.
Collapse
Affiliation(s)
- Muhammad Zia Ur Rahman
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
| | | | - Víctor Leiva
- Escuela de Ingeniería Industrial, Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Carlos Martin-Barreiro
- Facultad de Ciencias Naturales y Matemáticas, ESPOL, Guayaquil, Ecuador
- Facultad de Ingeniería, Universidad Espíritu Santo, Samborondón, Ecuador
| | - Muhammad Imran
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
- Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Muhammad Tanveer Riaz
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
| | - Cecilia Castro
- Centre of Mathematics, Universidade do Minho, Braga, Portugal
| |
Collapse
|
|
2
|
Methods in Medicine CAM. Retracted: IoT-Based Smart Health Monitoring System for COVID-19 Patients. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:9824131. [PMID: 37946945 PMCID: PMC10631902 DOI: 10.1155/2023/9824131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/8591036.].
Collapse
|
|
3
|
Chatterjee A, Prinz A, Riegler MA, Das J. A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care. BMC Health Serv Res 2023; 23:1047. [PMID: 37777722 PMCID: PMC10543863 DOI: 10.1186/s12913-023-10047-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/20/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. OBJECTIVE The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. METHODS The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. RESULTS Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). CONCLUSIONS Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.
Collapse
Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technology, Centre for e-Health, University of Agder, Grimstad, Norway.
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | - Andreas Prinz
- Department of Information and Communication Technology, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Michael A Riegler
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Jishnu Das
- Department of Information Systems, Centre for e-Health, University of Agder, Kristiansand, Norway
| |
Collapse
|
|
4
|
Deebak BD, Al-Turjman F. EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats. SENSORS (BASEL, SWITZERLAND) 2023; 23:2995. [PMID: 36991706 PMCID: PMC10051552 DOI: 10.3390/s23062995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/11/2022] [Accepted: 12/30/2022] [Indexed: 06/19/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.
Collapse
Affiliation(s)
- B. D. Deebak
- Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of Korea
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| |
Collapse
|
|
5
|
Rahman MZ, Akbar MA, Leiva V, Tahir A, Riaz MT, Martin-Barreiro C. An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients. Comput Biol Med 2023; 154:106583. [PMID: 36716687 PMCID: PMC9883984 DOI: 10.1016/j.compbiomed.2023.106583] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/28/2022] [Accepted: 01/22/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems. OBJECTIVE To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. METHODOLOGY We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal. RESULTS Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments. CONCLUSION Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.
Collapse
Affiliation(s)
- Muhammad Zia Rahman
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan.
| | - Muhammad Azeem Akbar
- Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland.
| | - Víctor Leiva
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
| | - Abdullah Tahir
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
| | - Muhammad Tanveer Riaz
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan; Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
| | - Carlos Martin-Barreiro
- Faculty of Natural Sciences and Mathematics, Escuela Superior Politécnica del Litoral ESPOL, Guayaquil, Ecuador; Faculty of Engineering, Universidad Espíritu Santo, Samborondón, Ecuador
| |
Collapse
|
|
6
|
Dami S. Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic. World J Clin Cases 2022; 10:9207-9218. [PMID: 36159404 PMCID: PMC9477683 DOI: 10.12998/wjcc.v10.i26.9207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/19/2022] [Accepted: 07/25/2022] [Indexed: 02/05/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).
Collapse
Affiliation(s)
- Sina Dami
- Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran 1468763785, Iran
| |
Collapse
|
|
7
|
The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|
|
8
|
M J, G CPL, S R, K T. Internet of Things (IOT) based Patient health care Monitoring System using electronic gadget. 2022 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS) 2022:487-490. [DOI: 10.1109/iciccs53718.2022.9788464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Jenifer M
- Kebri Dehar University,School of Engineering and Technology,Department of Computer Science and IT,Kebri Dehar,Ethiopia
| | - Charlyn Pushpa Latha G
- Saveetha Institute of Medical and Technical Sciences,Saveetha School of Engineering,Department of IT,Chennai,India
| | - Rinesh S
- Institute of Technology, Jigjiga University,School of Engineering,Department of Computer Science,Jigjiga,Ethiopia
| | - Thamaraiselvi K
- Malla Reddy College of Engineering,Department of CSE,Secunderabad,India
| |
Collapse
|
|
9
|
Rahman MZU, Raza AH, AlSanad AA, Akbar MA, Liaquat R, Riaz MT, AlSuwaidan L, Al-Alshaikh HA, Alsagri HS. Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7586-7605. [PMID: 35801437 DOI: 10.3934/mbe.2022357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.
Collapse
Affiliation(s)
- Muhammad Zia Ur Rahman
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Ali Hassan Raza
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Abeer Abdulaziz AlSanad
- Imam Mohammad Ibn Saud Islamic University, Information Systems Department, Riyadh 11432, Saudi Arabia
| | - Muhammad Azeem Akbar
- Lappeenranta University of Technology, Department of Information Technology, Lappeenranta 53851, Finland
| | - Rabia Liaquat
- U.S.-Pakistan Centre for Advanced Studies in Energy (USPCAS-E), National University of Sciences & Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
| | - Muhammad Tanveer Riaz
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Lulwah AlSuwaidan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
| | | | - Hatoon S Alsagri
- Imam Mohammad Ibn Saud Islamic University, Information Systems Department, Riyadh 11432, Saudi Arabia
| |
Collapse
|
|
10
|
Self-Oxygen Regulator System for COVID-19 Patients Based on Body Weight, Respiration Rate, and Blood Saturation. ELECTRONICS 2022. [DOI: 10.3390/electronics11091380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
One of the symptoms that appears in patients with COVID-19 is hypoxia or a lack of oxygen in the body’s tissues or cells below the proper level. One of the methods used to treat hypoxia is to provide oxygen to the patient. Another device that is needed in oxygen therapy for the patient is an oxygen regulator. An oxygen regulator is needed to regulate the volume of oxygen released to the patient. Currently, the control of oxygen flow by the regulator is still done manually. Therefore, in this study, an oxygen regulator was designed that has the ability to regulate the volume of oxygen output based on body weight, respiration rate, and blood saturation. Using these three parameters, the volume of oxygen to be released is adjusted according to the patient’s needs. The system consists of a temperature sensor, mlx90614, and an oxygen saturation sensor, Max30102. The data from the two sensors are processed using microcontrollers to control the movement of the stepper motor as a regulator of the oxygen output volume. The test results show that the system can control the oxygen regulator automatically with a delta error of 0.5–1 L/min. This device is expected to be used for COVID-19 patients who are undergoing self-isolation or who are outpatients.
Collapse
|
|
11
|
Wang X, Li J, Liu Q, Zhao W, Li Z, Wang W. Generative Adversarial Training for Supervised and Semi-supervised Learning. Front Neurorobot 2022; 16:859610. [PMID: 35401139 PMCID: PMC8988301 DOI: 10.3389/fnbot.2022.859610] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/25/2022] [Indexed: 11/18/2022] Open
Abstract
Neural networks have played critical roles in many research fields. The recently proposed adversarial training (AT) can improve the generalization ability of neural networks by adding intentional perturbations in the training process, but sometimes still fail to generate worst-case perturbations, thus resulting in limited improvement. Instead of designing a specific smoothness function and seeking an approximate solution used in existing AT methods, we propose a new training methodology, named Generative AT (GAT) in this article, for supervised and semi-supervised learning. The key idea of GAT is to formulate the learning task as a minimax game, in which the perturbation generator aims to yield the worst-case perturbations that maximize the deviation of output distribution, while the target classifier is to minimize the impact of this perturbation and prediction error. To solve this minimax optimization problem, a new adversarial loss function is constructed based on the cross-entropy measure. As a result, the smoothness and confidence of the model are both greatly improved. Moreover, we develop a trajectory-preserving-based alternating update strategy to enable the stable training of GAT. Numerous experiments conducted on benchmark datasets clearly demonstrate that the proposed GAT significantly outperforms the state-of-the-art AT methods in terms of supervised and semi-supervised learning tasks, especially when the number of labeled examples is rather small in semi-supervised learning.
Collapse
Affiliation(s)
- Xianmin Wang
- Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
| | - Jing Li
- Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
- State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
- *Correspondence: Jing Li
| | - Qi Liu
- Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
| | - Wenpeng Zhao
- Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
| | - Wenhao Wang
- State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
|
12
|
OUP accepted manuscript. JOURNAL OF PHARMACEUTICAL HEALTH SERVICES RESEARCH 2022. [DOI: 10.1093/jphsr/rmac007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
|