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Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy:Existing challenges and proposed enhancements

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World Journal of Diabetes 2025年第4期16卷 270-283页

作者： Chao-Fan Sun Yu-Han Lin Guo-Xing Ling Hui-Juan Gao Xing-Zhong Feng Chun-Quan Sun Department of Endocrinology Tsinghua University Yuquan HospitalBeijing 100040China Dongzhimen Hospital Beijing University of Chinese MedicineBeijing 100700China Office of Drug Clinical Trial Institution Health Management Center(Preventive Treatment of Disease)Tsinghua University Yuquan HospitalBeijing 100040China

BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical *** To conduct a comprehensive systematic review and rigorous evaluation of prediction models for *** A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October *** included and excluded criteria were applied by the researchers to screen the *** investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment *** were resolved through consultation with a third *** from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling ***,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment *** The systematic review included 14 studies with a total of 26 *** area under the receiver operating characteristic curve of the 26 models was *** studies had high risks of bias,mainly due to participants,outcomes,and *** most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood *** The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of *** studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.

关键词： Diabetic peripheral neuropathy predictive models Systematic review Risk factors Prognostic risk

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Improving Routine Immunization Coverage Through Optimally Designed predictive models

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Computers, Materials & Continua 2022年第1期70卷 375-395页

作者： Fareeha Sameen Abdul Momin Kazi Majida Kazmi Munir A Abbasi Saad Ahmed Qazi Lampros K Stergioulas Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Department of Pediatrics and Child Health Aga Khan UniversityKarachi74800Pakistan Surrey Business School University of SurreyGuildfordGU27XHUnited Kingdom Neurocomputation Lab National Centre of Artificial IntelligenceNED University of Engineering&TechnologyKarachi75270Pakistan Faculty of IT and Design The Hague University of Applied Sciences2521 ENThe HagueThe Netherlands

Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the *** being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine *** this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing *** predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 *** design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem *** optimization of predictive model is obtained through selection of significant features and removing data *** machine learning algorithms were applied for prediction of defaulting children for the next immunization *** results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%*** main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting *** information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.

关键词： Machine learning predictive models routine immunization vaccine coverage pakistan optimization SMOTE

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Development of predictive models for egg freshness and shelf-life under different storage temperatures

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Food Quality and Safety 2021年第4期5卷 344-350页

作者： Chunli Quan Qian Xi Xueping Shi Rongwei Han Qijing Du Fereidoun Forghani Chuanyun Xue Jiacheng Zhang Jun Wang College of Food Science and Engineering Qingdao Agricultural UniversityQingdaoChina College of Life Science Tarim UniversityAlarChina College of Animal Science and Technology Qingdao Agricultural UniversityQingdaoChina Shandong Engineering Technology Research Center of Food Quality and Safety Control QingdaoChina Molecular Epidemiology Inc.Lake Forest ParkWAUSA

The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen *** were carried out at different storage temperatures for a total period of 29-32 *** data were collected and fitted in to Arrhenius equation for egg freshness,while the HU data were applied to a probability model for shelf-life *** results showed that egg weight,albumen height,and HU decreased significantly,while albumen pH increased with the extension of storage *** higher the storage temperature,the faster the egg quality *** addition,the bias factor,accuracy factor,and the standard error of prediction were selected to verify the developed quality *** rescaled R-square statistic,the Hosmer-Lemeshow goodness-of-fit statistic,and the receiver operating characteristic curve were used to evaluate the goodness-of-fit of the developed probability model for the shelf-life of eggs,which indicated that the presented predictive models can be used to assess egg freshness and predict shelf-life during different storage temperatures.

关键词： Eggs predictive models probability model shelf-life freshness

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Comparative Study of Probabilistic and Least-Squares Methods for Developing predictive models

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Open Journal of Applied Sciences 2024年第7期14卷 1775-1787页

作者： Boribo Kikunda Philippe Thierry Nsabimana Jules Raymond Kala Jeremie Ndikumagenge Longin Ndayisaba Centre de Recherche en Infrastructure Environnement et Technologie (CRIET) Faculty of Engineering Sciences Universit du Burundi Bujumbura Burundi Faculty of Sciences Universit Catholique de Bukavu (UCB) Bukavu DR Congo Management Computer Department Institut suprieur Pdagogique de bukavu (ISP/Bukavu) Bukavu DR Congo

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

关键词： predictive models Least Squares Bayesian Estimation Methods

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Construction and validation of predictive models for treatment-emergent central sleep apnea in adult OSA patients

Construction and validation of predictive models for treatme...

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中国睡眠研究会第16届全国学术年会暨湖北科学技术学术年会

作者： Mengdie Yang Yiran Cui Cheng Peng Yan Wang Baoyuan Chen Tianjin Medical University General Hospital

Objective Constructing and comparing treatment-emergent central sleep apnea(TECSA) prediction models based on different machine learning algorithms can help healthcare professionals quickly and accurately assess the risk of developing the *** A retrospective study was conducted in which patients were divided into the TECSA group(59 cases) and the non-TECSA group(161 cases) based on the occurrence of TECSA after continuous positive airway pressure(CPAP).Significant factors identified by single-factor analysis were included in the *** training set utilized four algorithms:logistic regression(LR),support vector machine(SVM),random forest(RF) and extreme gradient boosting(XGBoost) to construct the TECSA prediction *** validation set assessed the predictive performance of the models using metrics such as the area under the receiver operating characteristic(ROC) curve,accuracy,sensitivity,specificity,Kappa value and F1 *** Singlefactor analysis revealed that age,BMI,gender,AHI,CAI,MAI,ArI and MSpO2were statistically significant between the two *** TECSA prediction models constructed based on four algorithms,LR,SVM,RF and XGBoost performed well,with the RF model exhibiting the best predictive *** The significant predictors identified by the machine learning algorithms can provide valuable information for predicting the risk of TECSA in OSA patients undergoing CPAP *** developed models serve as effective predictive tools for healthcare professionals in selecting treatment options.

关键词： Obstructive sleep apnea Treatment-emergent central sleep apnea Continuous positive airway pressure ventilation predictive models

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Infrared Thermography-Based predictive Model for Syndrome Differentiation of Chaihu Guizhi Ganjiang Decoction

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Proceedings of Anticancer Research 2025年第1期9卷 27-36页

作者： Huisi Hong Yiming Yuan Na Li Xiaoxing Huang Leyu Li Tingting Zeng Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine Zhongshan 528401Guangdong ProvinceChina

Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction ***:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April *** surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography *** component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome ***:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction *** identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for *** neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation *** was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score ***:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in *** represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and *** study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.

关键词： Infrared thermography technology Chaihu Guizhi Ganjiang Decoction syndrome Syndrome differentiation and treatment Data analysis predictive models Modernization of traditional Chinese medicine

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No Reference Image Sharpness Assessment Based on Global Color Difference Variation

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Chinese Journal of Electronics 2024年第1期33卷 293-302页

作者： Chenyang SHI Yandan LIN School of Artificial Intelligence Anhui Polytechnic University School of Mechanical Engineering Anhui Polytechnic University Department of Light Sources and Illuminating Engineering Fudan University Academy for Engineering & Technology Fudan University

Image quality assessment(IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a valid no reference IQA(NR-IQA) model for color image sharpness assessment is proposed based on local color difference map in a color space. In the proposed model, the absolute color difference variation and relative color difference variation are combined to evaluate sharpness in YIQ color space(a color coordinate system for the development of the United States color television system). The difference between sharpest and blurriest spot of an image is represented by the absolute color difference variation, and relative color difference variation expresses the variation in the image content. Extensive experiments are performed on five publicly available benchmark synthetic blur databases and two real blur databases, and the results prove that the proposed model work better than the other state-of-the-art and latest NR-IQA models for the prediction accuracy on blurry images. Besides, the model maintains the lowest computational complexity.

关键词： Image quality TV Image color analysis Databases Computational modeling Color predictive models

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Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

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Chinese Journal of Electronics 2024年第6期33卷 1412-1420页

作者： Binhao HU Jianpeng ZHANG Hongchang CHEN School of Cyber Science and Engineering Zhengzhou University National Digital Switching System Engineering & Technological R&D Center (NDSC)

With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to the unseen entities that are not observed during training but appear during testing. Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and use a hyperbolic space-based distance model to learn structural information, then integrate the two types of information together. We evaluate our model by performing link prediction experiments on standard datasets. The experimental results show that our model achieves better performances than state-of-the-art methods on two standard datasets.

关键词： Training Semantics Knowledge graphs predictive models Vectors Robustness Explosions Standards Testing Context modeling

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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Relating estimates of wood properties of birch to stem form, age and species

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Journal of Forestry Research 2024年第3期35卷 1-11页

作者： Grace Jones Maria Ulan Mateusz Liziniewicz Johan Lindeberg Stergios Adamopoulos Teagasc Forestry Development Department Ashtown Research CentreDublin 15 D15DY05Ireland Research Institutes of Sweden(RISE) GoteborgSweden The Forest Research Institute of Sweden(Skogforsk) Ekebo 225026890SvalovSweden Department of Forestry and Wood Technology Linnaeus University35195VaxjoSweden Department of Forest Biomaterials and Technology Swedish University of Agricultural SciencesBox 700875007 UppsalaSweden

Birch has long suffered from a lack of active forest management,leading many researchers to use mate-rial without a detailed management *** collected from three birch(Betula pendula Roth,*** Ehrh.)sites in southern Sweden were analyzed using regression analysis to detect any trends or differences in wood proper-ties that could be explained by stand history,tree age and stem *** sites were genetics trials established in the same *** of acoustic velocity(AV)from non-destructive testing(NDT)and predicted AV had a higher correlation if data was pooled across sites and other stem form factors were considered.A subsample of stems had radial profiles of X-ray wood density and ring width by year created,and wood density was related to ring number from the pith and ring *** seemed likely that wood density was negatively related to ring width for both birch *** models had slight improvements if site and species were included,but only the youngest site with trees at age 15 had both birch *** paper indicated that NDT values need to be considered separately,and any predictive models will likely be improved if they are specific to the site and birch species measured.

关键词： Acoustic velocity Non-destructive testing predictive models Regression analysis Wood density

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