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Objective:To construct a multi-dimensional surgical equipment management and control platform based on artificial intelligence and Internet of Things(AIoT)to assist with the refinement and intelligent management medical equipment in hospital operating rooms.Methods:A multi-dimensional surgical equipment control platform based on AIoT was established by integrating the Internet of Things(IoT),big data analysis,indoor positioning technology,artificial intelligence(AI)technology and other technologies to collect real-time process data of surgical equipment such as endoscopy and electrosurgical,and to open up the relationships among information systems relating to surgical equipment,such as hospital information system(HIS),laboratory information system(LIS),radiology information system(RIS)and operation anesthesia management system(OAMS),so as to provide technical support for efficiency analysis,benefit analysis and assets management of surgical equipment.The platform was composed of 3 layers:data extraction layer,data engine layer and AI data analysis layer,including 4 functional modules:automatic data acquisition,deep data fusion,data mining and analysis and data visualization.Results:This platform was launched in Shanghai Municipal Hospital of Traditional Chinese Medicine in June 2022,and had realized achieving intelligent daily management such as indoor positioning of operating room equipment,one click inventory.A set of performance analysis method based on IoT and integrated with information systems was established to automatically count the utilization efficiency and cost-effectiveness of key surgical equipment to realize intelligent service,intelligent management,and digital operation.Conclusion:The construction and application of this platform improved the efficiency of medical equipment in operating rooms,reduced the cost and increased the efficiency,assisted in the refinement and intelligent management of hospital surgical equipment,and provided data support for scientific decision-making of hospital managers.
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Food and fibre, two of humanity's most fundamental requirements, are met by agriculture. In the last century, new farming methods have been introduced, such as the Green Revolution, which has enabled agriculture to keep up with the increasing demand for food and other agricultural goods. But population growth, rising income levels, and increased food demand will probably put more stress on the planet's natural resources. As the detrimental effects of agriculture on the environment become more widely acknowledged, new methods and strategies need to be able to meet future food needs while preserving or lessening the environmental footprint of agriculture. Informed management decisions aiming at increasing crop production could be made with the help of emerging technologies like artificial intelligence (AI), Internet of Things (IoT), big data analysis, and geospatial technology. Many scientists, engineers, agronomists, and researchers use a variety of technologies each year to boost agricultural output while minimising pollution, yet these efforts have a negative environmental impact. Precision agriculture examines how technology might be applied to enhance agricultural practises relative to traditional methods while minimising negative environmental effects. Precision agriculture greatly benefits from the deployment of remote sensing technologies, which also presents new chances to enhance agricultural practises. Geographically, latitude and longitude data can be recorded for field data (slope, aspect, nutrients, and yield) using the global positioning system (GPS). Because of its ability to continuously determine and record the right position, it can build a larger database for the user. Geographic Information Systems (GIS), which can handle and store these data, are needed for the additional analysis. This review will offer you an overview of Remote Sensing technology, GPS, and GIS, and how it might be used for precision agriculture.
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Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.
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Humans , Arrhythmias, Cardiac/diagnosis , Electrocardiography/methods , AlgorithmsABSTRACT
The establishment of mental health assessment system provides a new way for the early diagnosis of mental health problems, in view of the growing population of mental diseases and problems and the uneven distribution of mental health resources. In the mental health assessment system, intelligent assistant diagnosis can assist or help psychiatrists improve their work efficiency. Intelligent assistant diagnosis provides technical support for predictive screening and auxiliary diagnosis of mental health problems. It is an intelligent diagnosis research based on big data analysis and machine learning in mental health assessment system. This article mainly reviews the application methods, the application progress in the field of mental health, as well as related technical issues and safety issues, and prospects the future research development.
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Objective To study the research progress of big data analysis in gait biomechanics. Methods Based on the scientific and technological literature related to big data analysis in gait biomechanics during the year 2011-2020 as the research object, content analysis method was used to analyze and discuss from four aspects, including topic structure, hierarchy level, model type and analysis technology. On this basis, the future research of gait biomechanics big data analysis was prospected. Results The application of big data analysis in gait biomechanics mainly involves five research directions, namely, intervention and rehabilitation, exercise training, prosthesis design and evaluation, understanding of etiology and diagnosis, understanding of human movement characteristics. Big data analysis in gait biomechanics is divided into three levels, of which descriptive analysis is the most used type, accounting for about 41%. The models and specific techniques of big data analysis in gait biomechanics field were reviewed. Topological data analysis is a promising big data exploration tool for future research. Conclusions Big data technology has great potential in gait biomechanics and clinical medicine research.
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BACKGROUND: Platelet-rich plasma has been shown to promote tissue repair and regeneration. OBJECTIVE: To evaluate the current status and hotspot of platelet-rich plasma globally by bibliometrics. METHODS: In the database of web of science, “platelet rich plasma” was used as keyword to research relevant researches published before December 25, 2018. The retrieved articles were indexed and analyzed according to country/region, institution, publication year, and publication name. Excel 2007 was used for descriptive statistics. VOS viewer (Leiden University, Netherlands) software was used to analyze and draw all the retrieved items. The main evaluation indicators were co-occurrence relationship, mutual citation relationship, co-citation relationship, and cooperation relationship map. RESULTS AND CONCLUSION: (1) Totally 8 499 studies on platelet-rich plasma were retrieved, and the number and citations increased gradually. (2) Number of studies on platelet-rich plasma ranked top 3 countries are the United States, Italy, and Japan. (3) The subject direction and research fields of platelet-rich plasma mainly involve five aspects: basic research on growth factor function, research on bone regeneration, clinical research in cartilage or osteoarthritis, platelet function research, and stem cells. (4) The analysis results based on big data suggest that platelet-rich plasma is mainly applied in orthopedics, especially cartilage regeneration and osteoarthritis, which will be the focus of future research and technology investment.
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With the continuous improvement of living levels, people's demands for health services present a various and personalized characteristics, and more and more people choose to carry out routine inspection and treatment in health care institutions. At present, China's health service market mainly adopts the hospital as the main body and adopts the third party health operating organization as subsidiary role. The health management service mode of whole process innovatively achieved seamless joint between health information operation services and hospital information platform, and realized interconnection between data center of health information operation service organization and hospital information platform. Through analyzed big data to realize early warning and forecast for disease. And patients were used as clue of prime index to put case history, health, consumption, service, feedback and other information in series. And a whole dimensionality information base of patients were established. And the integrated health archive was used as carrier to provide information base for the interconnection between profession and service. It can not only provide convenient medical experience and extended medical service for patients, but also provide clinical reference and clinical research data for doctors. It is a application of new type whole course health management service mode.