Dataset Information

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

ABSTRACT: Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.

SUBMITTER: Pei FQ

PROVIDER: S-EPMC5722377 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

Pei Feng-Que FQ Li Dong-Bo DB Tong Yi-Fei YF He Fei F

PloS one 20171208 12

Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large ...[more]

PMID: 29220393

Similar Datasets

Project description:BackgroundThe COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior.ObjectiveIn this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19.MethodsWe designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression.ResultsWe extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval.ConclusionsThe proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Project description:BackgroundSuicide rates continue to rise across the United States, galvanizing the need for increased suicide prevention and intervention efforts. The Zero Suicide (ZS) model was developed in response to this need and highlights four key clinical functions of high-quality health care for patients at risk of suicide. The goal of this quality improvement study was to understand how six large health care systems operationalized practices to support these functions-identification, engagement, treatment and care transitions.MethodsUsing a key informant interview guide and data collection template, researchers who were embedded in each health care system cataloged and summarized current and future practices supporting ZS, including, (1) the function addressed; (2) a description of practice intent and mechanism of intervention; (3) the target patient population and service setting; (4) when/how the practice was (or will be) implemented; and (5) whether/how the practice was documented and/or measured. Normalization process theory (NPT), an implementation evaluation framework, was applied to help understand how ZS had been operationalized in routine clinical practices and, specifically, what ZS practices were described by key informants (coherence), the current state of norms/conventions supporting these practices (cognitive participation), how health care teams performed these practices (collective action), and whether/how practices were measured when they occurred (reflexive monitoring).ResultsThe most well-defined and consistently measured ZS practices (current and future) focused on the identification of patients at high risk of suicide. Stakeholders also described numerous engagement and treatment practices, and some practices intended to support care transitions. However, few engagement and transition practices were systematically measured, and few treatment practices were designed specifically for patients at risk of suicide.ConclusionsThe findings from this study will support large-scale evaluation of the effectiveness of ZS implementation and inform recommendations for implementation of high-quality suicide-related care in health care systems nationwide.

Project description:OBJECTIVES:To examine patient and staff views, experiences and acceptability of a UK primary care online consultation system and ask how the system and its implementation may be improved. DESIGN:Mixed-method evaluation of a primary care e-consultation system. SETTING:Primary care practices in South West England. METHODS:Qualitative interviews with 23 practice staff in six practices. Patient survey data for 756 e-consultations from 36 practices, with free-text survey comments from 512 patients, were analysed thematically. Anonymised patients' records were abstracted for 485 e-consultations from eight practices, including consultation types and outcomes. Descriptive statistics were used to analyse quantitative data. Analysis of implementation and the usage of the e-consultation system were informed by: (1) normalisation process theory, (2) a framework that illustrates how e-consultations were co-produced and (3) patients' and staff touchpoints. RESULTS:We found different expectations between patients and staff on how to use e-consultations 'appropriately'. While some patients used the system to try and save time for themselves and their general practitioners (GPs), some used e-consultations when they could not get a timely face-to-face appointment. Most e-consultations resulted in either follow-on phone (32%) or face-to-face appointments (38%) and GPs felt that this duplicated their workload. Patient satisfaction of the system was high, but a minority were dissatisfied with practice communication about their e-consultation. CONCLUSIONS:Where both patients and staff interact with technology, it is in effect 'co-implemented'. How patients used e-consultations impacted on practice staff's experiences and appraisal of the system. Overall, the e-consultation system studied could improve access for some patients, but in its current form, it was not perceived by practices as creating sufficient efficiencies to warrant financial investment. We illustrate how this e-consultation system and its implementation can be improved, through mapping the co-production of e-consultations through touchpoints.

Dataset Information

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

Publications

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets