Construction of patient service system based on QFD in internet of things

Huang, Anzhong; Cao, Jie; Hui-mei, Zhang

doi:10.1007/s11227-020-03359-y

Cited by 4 publications

(10 citation statements)

References 26 publications

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“…Studies included in the review ranged from 2011 to 2020, one being from 2011, one from 2015, two from 2018, one from 2019, and three from 2020, of which three were from China, two from USA, one from Mexico, and one from Thailand, as can be seen in Table 4. These studies are aimed at recognizing body posture in bed (e.g., [6,7,11,12]) and identifying potential risk of developing pressure ulcers (e.g., [8][9][10]) and providing some type of alerts or recommendations.…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…Data analysis was performed using distinct algorithms. According to Tables 5 and 6, neural networks were used in two studies [8,11], support vector machines (SVM) were used in three studies [6,7,11], and XGBoost [9], RF [10], DT [11] and deep learning [12] were each used in one study. Some studies did not describe how they used the algorithm output, although other studies referred alerts or warnings to caregivers to intervene when the patient was absent or when he or she stayed in the same position for too long.…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…Acquiring this data usually depends exclusively on human intervention. Recently, researchers have been exploring ways to integrate machine learning (ML) and the internet of things (IoT) into healthcare systems, to produce indicators/alerts useful to healthcare professionals [6][7][8][9][10][11][12]. Healthcare professionals cannot care for PU patients based only on predisposing factors, nor based only on humanless data.…”

Section: Introductionmentioning

confidence: 99%

“…Healthcare professionals cannot care for PU patients based only on predisposing factors, nor based only on humanless data. Posture recognition can identify when localized parts of the body are under continuous pressure, such as when patients are recovering from surgery, so blood supply to those areas is hindered and a pressure sore develops consequently [2,[5][6][7][8][9][10][11][12]. For example, after identifying that a bedridden pressure ulcer patient is in the same position for a specific amount of time, based on sensors attached in the mattress (humanless data acquisition), the adequate time to change his position may differ based on gender, age, Informatics 2021, 8, 76 2 of 13 comorbidities, and other aspects (human intervention data acquisition).…”

Section: Introductionmentioning

confidence: 99%

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Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

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Section: Discussion and Findingsmentioning

confidence: 99%

Section: Discussion and Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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“…The following is the House of Quality matrix in the Temperature Information System Tool design [9]- [12].…”

Section: 𝐾𝑡 = $(𝐵𝑡𝑖 𝑥 𝐻𝑖)mentioning

confidence: 99%

Designing of temperature control for agitator machine using Internet of Thing

Paduloh

Fatahillah

Ramadhan

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Aristek Highpolymer is a company that produces emulsion polymers and synthetic resins. In the production process, the R301 agitator has often experienced overheating, which disrupted the production process and caused damage to the mixer engine motor. Currently, checking the temperature on the Agitator R301 has been done manually using a thermo-gun and often caused the information to be hampered and several times caused the stirrer motor to go off. For this reason, this study designed a tool that could facilitate checking and reporting if there was an increase in engine temperature. This tool utilized an information system using the concept of Web-Based Internet of Things (IoT). The information system was designed using UML, and the temperature reader was designed using Arduino Uno, Max6675 temperature sensor, and GSM SIM 800L module using the Fritzing application. Product design was done using QFD to get the design of the control device according to the customer’s wishes. The research and experiments obtained a machine temperature controller that could provide information directly to decision-makers if an abnormal event occurred in the machine. It could also control the engine temperature so that it increased productivity and decreased maintenance costs for the agitator machine.

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Big data research in nursing: A bibliometric exploration of themes and publications

Li,

Du,

et al. 2023

J of Nursing Scholarship

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AimsTo comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.DesignBibliometric analysis.MethodsThe quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.ResultsThe research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.ConclusionsThe fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.Clinical RelevanceThis study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

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Construction of patient service system based on QFD in internet of things

Cited by 4 publications

References 26 publications

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Designing of temperature control for agitator machine using Internet of Thing

Big data research in nursing: A bibliometric exploration of themes and publications

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