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Identification and predictive analysis for participants at ultra-high risk of psychosis:A comparison of three psychometric diagnostic interviews

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World Journal of Clinical Cases 2022年第8期10卷 2420-2428页

作者： Peng Wang Chuan-Dong Yan Xiao-Jie Dong Lei Geng Chao Xu Yun Nie Sheng Zhang Department of Psychological Rehabilitation the Affiliated Wuhan Mental Health CenterTongji Medical College of Huazhong University of Science&TechnologyWuhan 430022Hubei ProvinceChina

BACKGROUND An accurate identification of individuals at ultra-high risk(UHR)based on psychometric tools to prospectively identify psychosis as early as possible is required for indicated preventive *** diagnostic comparability of several psychometric tools,including the comprehensive assessment of at risk mental state(CAARMS),the structured interview for psychosis-risk syndrome(SIPS)and the bonn scale for the assessment of basic symptoms(BSABS),is *** To address the psychometric comparability of CAARMS,SIPS and BSABS for subjects who are close relatives of patients with *** In total,189 participants aged 18-58 years who were lineal relative by blood and collateral relatives by blood up to the third degree of kinship of patients with schizophrenia were interviewed in the period of May 2017 to January *** of the participants diagnosed schizophrenia were *** the participants were assessed for a UHR state by three psychometric tools(CAARMS,SIPS and BSABS).The psychometric diagnosis results included at risk of psychosis(UHR+),not at risk of psychosis(UHR-)and *** and clinical characteristics were also *** inter-rater agreement was assessed for evaluation of the coherence of the three *** rates for UHR+subjects to psychosis within 2 years were also *** The overall agreement percentages were 93.12%,92.06%and 93.65%of CAARMS and SIPS,SIPS and BSABS and CAARMS and BSABS,*** overall agreement percentage of the relative functional impairment of the three groups(UHR+,not at risk of psychosis and psychosis)were 89.24%,86.36%and 88.12%,*** inter-rater reliability of the CAARMS,SIPS and BSABS total score was 0.90,0.89 and *** inter-rater reliability was very good to excellent for all the subscales of these three *** CAARMS,SIPS and BSABS,the kappa coefficient about UHR criteria agreement was 0.87,0.84 and 0.82,respectively(P<0.

关键词： Psychosis Ultra-high risk Psychosis-Risk syndrome Psychometric diagno-stic predictive analysis

Recent progress and trends in predictive visual analytics

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Frontiers of Computer Science 2017年第2期11卷 192-207页

作者： Junhua LU Wei CHEN Yuxin MA Junming KE Zongzhuang LI Fan ZHANG Ross MACIEJEWSKI State Key Lab of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China College of Science Zhejiang University of Technology Hangzhou 310023 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.

关键词： predictive visual analytics visualization visual analytics data mining predictive analysis

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

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Computers, Materials & Continua 2020年第9期64卷 1473-1490页

作者： Bingjie Yan Jun Wang Zhen Zhang Xiangyan Tang Yize Zhou Guopeng Zheng Qi Zou Yao Lu Boyi Liu Wenxuan Tu Neal Xiong School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China College of Humanities and Communication Hainan UniversityHaikou570228China University of Chinese Academy of Science Beijing100049China National University of Defense Technology Changsha410073China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahUSA

New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the *** understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding ***,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation *** address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural *** work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as ***,this work uses the goodness of fitting to evaluate the fitting effect of the *** show that the proposed approach has a smaller prediction deviation and a better fitting *** with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

关键词： COVID-19 LSTM model predictive analysis

A compartmental model to investigate the dynamics of the COVID-19 pandemic:A case study in five countries

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International Journal of Biomathematics 2021年第5期14卷 51-63页

作者： Ofosuhene O.Apenteng Bismark Oduro Isaac Owusu-Mensah Division for Global Surveillance Research Group for Genomic Epidemiology National Food Institute Technical University of Denmark Kongens LyngbyDenmark Department of Mathematics and Physical Sciences California University of Pennsylvania California Pennsylvania 15419USA Department of Science Education University of Education Winneba Mampong-AshantiGhana

Coronavirus Disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections,was declared a pandemic by the WHO on March 12,*** April 10,2020,it has spread to almost 215 UN Territories,affected more than 1,600,000 people,and become fatal to more than 100,000 *** propose a compartmental model to investigate the dynamics of the COVID-19 pandemic and analyze the effects of governments and health officials’intervention strategies,using data in the USA,Italy,Spain,Germany,and *** are the countries with the most confirmed cases of the COVID-19 as of April 11,*** perform parameter estimations,sensitivity,and predictive analysis and compare the COVID-19 trend in these understudy *** on the model,we compute the basic reproduction number of the pandemic in these *** results indicate that the most sensitive parameters are the contact rate and degree of intervention;these parameters have high value in containing the *** basic reproduction number in each of the countries under study is more than *** on our findings,the COVID-19 pandemic will continue to spread;but higher compliance with the intervention strategies will help minimize the disease’s spread.

关键词： COVID-19 disease parameter estimation sensitivity analysis predictive analysis

Asymmetric outcomes in bilateral maxillary impacted tooth extractions:A case report

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World Journal of Radiology 2024年第10期16卷 608-615页

作者： Han Liu Fang Wang Yan-Li Tang Xing Yan Department of Stomatology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China

BACKGROUND The extraction of maxillary impacted teeth is a common procedure in oral surgery,frequently complicated by oroantral *** less-experienced clinicians,accurately assessing the difficulty and associated risks of maxillary third molar extractions remain a significant *** SUMMARY We present a case involving disparate outcomes following bilateral extraction of maxillary third *** cone-beam computed tomography and three-dimensional software,we conducted a digital assessment of the factors contributing to extraction difficulty and risk,controlling for potential *** variables analyzed included alveolar bone volume,bone quality,crown-root angulation,and maxillary sinus mucosal ***,we introduce the novel concept of"tegmen bone"to quantitatively evaluate the bone mass between the teeth and the maxillary *** unique case,with differing outcomes on opposite sides of the same patient,provided an opportunity to minimize extraneous variables and focus on the local anatomical factors influencing the procedures,thereby improving the precision of our *** This case highlights the potential utility of predictive analysis in guiding the management of complex tooth extractions.

关键词： Cone-beam computed tomography Maxillary third molar Tegmen bone Digital analysis predictive analysis Case report

The Symbiotic Relationship Unraveling the Interplay between Technology and Artificial Intelligence(An Intelligent Dynamic Relationship)

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Journal of Energy and Power Engineering 2023年第2期17卷 63-68页

作者： Bahman Zohuri Farhang Mossavar-Rahmani Ageno School of Business Golden Gate UniversitySan Francisco 94105CaliforniaUSA Finance School of Business and Economics National UniversitySan Diego 92110CaliforniaUSA

This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and *** has advanced dramatically throughout the years,providing the groundwork for the rise of *** systems have achieved incredible feats in various disciplines thanks to advancements in computer power,data availability,and complex *** the other hand,society’s needs for efficiency,enhanced healthcare,environmental sustainability,and personalized experiences have worked as powerful accelerators for AI’s *** article digs into how technology empowers AI and how societal needs dictate its progress,emphasizing their symbiotic *** findings underline the significance of responsible AI research,which considers both technological prowess and ethical issues,to ensure that AI continues to serve the greater good.

关键词： Technology AI society evolution advancements computing power data availability algorithms efficiency healthcare environmental sustainability personalized experiences automation machine learning natural language processing image recognition predictive analysis cloud computing BD(big data) user experience innovation ethical considerations responsible AI development

Empirical Study on the Sustainable Development of Domestic Tourism

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Open Journal of Statistics 2020年第4期10卷 651-658页

作者： Shichang Shen School of Mathematics and Statistics Qinghai Nationalities University Xining China

Taking the number of domestic tourists as the research object, an appropriate model is established to analyze the sustainable development of China’s tourism industry. The number of domestic tourists in China from 1985 to 2015 was taken as a sample, and the ARIMA model was established using Eviews 6.0. Finally, the ARIMA(0, 2, 1) model is established. After testing, the model fits well (MAPE = 7.363) and the prediction accuracy is extremely high (99.56%). The ARIMA model’s short-term prediction of the number of tourists is reasonable.

关键词： Number of Tourists ARIMA Model predictive analysis