Fuzzy regression model clustering algorithm based assessment, analysis and acceptance of palliative care and psychological behaviors of elderly patients with cancer pain

Ke, Xi; Luo, Haiwei; He, Xiaoli; Mei-hua, WU; Wu, Guangfeng; Fang, Zhu Ting; Fengping, Gao; Zeng, Guihua

doi:10.1007/s10586-018-2391-9

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…e big data analytic process addresses risks and challenges that are presented in the psychological assessment system. In psychological quality assessment, big data analytics mostly identifies statistical data and psychological data for the further analysis process [9,10].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Influencing Factors of Big Data on College Students’ Positive Psychological Quality under Mobile Wireless Network

Jiang

Zhang

2022

Mobile Information Systems

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Psychological big data is observed based on behavior or habitual features of people for determining the influencing factors. The influencing factor information is used for analysis in treatment, recommendation, and diagnosis of psychological disorders, depression, etc. Identifying the influencing factors is challenging due to irregular behaviors and responses from young people. However, for organizing the quality of observation, this article introduces a behavioral pattern recognition method (BPRM) with associating quality (AQ) identification. This method observes the different day-to-day behaviors of young people and recurrently associates them. The observations are aided through conventional wireless networks for swift interconnection and information sharing. The association is organized based on the transfer learning state processing model. Based on the state processing, the behavior-based psychological data are classified as abnormal and normal. If the association throughout the state changes remains the same, then it is organized or else the new data are identified as an influencing factor. The state changes are validated using random observation intervals that result in series data associations. Based on the actual data extraction, the proposed method improves the prediction accuracy and reduces false rate and processing time, whereas it improves the organization precision.

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Section: Introductionmentioning

confidence: 99%

Prediction and Influencing Factors of Big Data on College Students’ Positive Psychological Quality under Mobile Wireless Network

Jiang

Zhang

2022

Mobile Information Systems

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“…To our knowledge, this is the first time a tool of this kind is presented in the literature. However, there are several recent studies in which clustering in older adults has been found to be effective in studying different targets [ 8 , 9 , 10 , 11 ], proving clustering’s potential as part of the COLAEVA feature. These studies, aside from not being in the same field, are carried out on a specific database, and therefore they lack the characteristic of finding new knowledge or searching for it with their own criteria, which is an aspect that we are able to add through COLAEVA.…”

Section: Introductionmentioning

confidence: 99%

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

Kerexeta

Beristáin

Álvarez

et al. 2021

Sensors

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The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.

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Research on Monitoring and Analysis of Operational Quality of SOA Service Based on Electric Power Enterprises

Huang

Lin

Shang

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Fuzzy regression model clustering algorithm based assessment, analysis and acceptance of palliative care and psychological behaviors of elderly patients with cancer pain

Cited by 4 publications

References 15 publications

Prediction and Influencing Factors of Big Data on College Students’ Positive Psychological Quality under Mobile Wireless Network

Prediction and Influencing Factors of Big Data on College Students’ Positive Psychological Quality under Mobile Wireless Network

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

Research on Monitoring and Analysis of Operational Quality of SOA Service Based on Electric Power Enterprises

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