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Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study

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中华医学杂志(英文版) 2024年第19期137卷 2024 Feb 26页

作者： Zheng Hongchen Liu Zhen Chen Yun Ji Ping Fang Zhengyu He Yujie Guo Chuanhai Xiao Ping Wang Chengwen Yin Weihua Li Fenglei Chen Xiujian Liu Mengfei Pan Yaqi Liu Fangfang Liu Ying He Zhonghu Ke Yang State Key Laboratory of Molecular Oncology Beijing Key Laboratory of Carcinogenesis and Translational Research Department of Genetics Peking University Cancer Hospital &amp Institute Beijing China Department of Ultrasound Peking University Shenzhen Hospital Shenzhen Guangdong China Shenzhen Key Laboratory for Drug Addiction and Medication Safety Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center Shenzhen Guangdong China Clinical Research Institute Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center Shenzhen Guangdong China Endoscopy Center Hua County People’s Hospital Anyang Henan China Endoscope Group Department of Gastroenterology Peking University Shenzhen Hospital Shenzhen Guangdong China Department of Pathology Peking University Shenzhen Hospital Shenzhen Guangdong China Hua County People’s Hospital Anyang Henan China Department of Pathology Hua County People’s Hospital Anyang Henan China

Background: Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic ***: We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled ***: This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750–0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570–0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various ***: This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

关键词： Early detection of cancer Cancer early diagnosis Gastric cancer diagnostic model Opportunistic screening Feeding behavior Weight loss Sex characteristics

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diagnostic model construction and example analysis of habitat degradation in enclosed bay: I. diagnostic model construction

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Chinese Journal of Oceanology and Limnology 2014年第3期32卷 626-635页

作者：于格孙芃刘光兴徐东晖丁光茂黄东仁 Key Laboratory of Marine Environmental Science and Ecology College of Environmental Sciences and EngineeringOcean University of China Fujian Provincial Ocean and Fisheries Monitoring Center

Presently, research is lacking regarding the diagnosis and evaluation of habitat degradation in enclosed bay systems. We established a diagnostic model for enclosed bay habitat degradation(EBHD model) using a multi-approach integrated diagnostic method in consideration of driving force-pressurestate-infl uence-response. The model optimizes the indicator standardization with annual average change rate of habitat degradation as the basic element, to refl ect accurately the impact of the change and speed of degradation on the diagnostic results, to quantify reasonably the contribution of individual diagnostic indicator to habitat degradation, and to solve the issue regarding the infl uence of subjective factors on the evaluation results during indicator scoring. We then applied the EBHD model for the Sansha Bay in Fujian Province, China, evaluated comprehensively the situation of habitat degradation in the bay, and screened out the major controlling factors in the study area. Results show that the diagnostic results are consistent in overall with the real situation of the study area. Therefore, the EBHD model is advantageous in terms of objectivity and accuracy, making a breakthrough in diagnosis and evaluation for habitat degradation in enclosed bay systems.

关键词： enclosed bay habitat degradation diagnostic model Sansha Bay

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The calculation of the circulation in South China Sea by a diagnostic model

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Acta Oceanologica Sinica 2008年第Z1期27卷 31-45页

作者： ZHOU Weidong YANG Yang DONG Danpeng South China Sea Institute of Oceanology Chinese Academy of Science Guangzhou 510301 China South China Sea Institute of Oceanology Chinese Academy of Science Guangzhou 510301 China South China Sea Institute of Oceanology Chinese Academy of Science Guangzhou 510301 China

A high resolved two-dimensional linear global diagnostic model combining with the dynamical calculation is used to calculate velocity field in the South China Sea(SCS). The study of model results shows that eddy diffusion does not change basic structure of circulation in the SCS and does not change the direction of invasive water, but changes the value of transport considerably especially in straits. The velocity field is not changed whether the wind stress is considered or not. This result shows the circulation is largely determined by a density field which well records most of the important contribution of the wind stress effect. Potential vorticity is calculated to testify the dynamics of the model results. The result shows that a good conservation of the nonlinear PV. This indicates most effects of the important nonlinear processes are well recorded in density and the nonlinear term is negligible so that the simplified model is reliable. The model results show the water exchanges between the SCS and open ocean or surrounding seas. Cold deep water invades through Luzon Strait and Warm shallow water is pushed out mainly through Karimata Straits. The model results also reveal the structure of the circulation in the SCS basin. In two circulations of upper and middle layers, a cyclonic one in the north and an anti-cyclonic one in the south, reflect the climatologic average of the circulation driven by monsoon. In the deep or bottom layer, these two circulations reflect the topography of the basin. Above the middle layer, invasive water enters westward in the north but the way of invasion of Kuroshio is not clear. Below the deep layer, a current goes down south near the east basin ,and invasive water enters in the basin from the west Pacific.

关键词： diagnostic model South China Sea potential vorticity circulation

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A new diagnostic model of primary open angle glaucoma based on FD-OCT parameters

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International Journal of Ophthalmology(English edition) 2018年第6期11卷 951-957页

作者： Ya-Jie Zheng Ying-Zi Pan Xue-Ying Li Yuan Fang Mei Li Rong-Hua Qia Yu Cai Department of Ophthalmology MEM Eye Care System Beijing 100039 China Department of Ophthalmology Peking University First Hospital Beijing 100034 China

AIM： To build a clinical diagnostic model of primary open angle glaucoma （POAG） using the normal probability chart of frequency-domain optical coherence tomography （FD-OCT）. METHODS： This is a cross-sectional study. Total 133 eyes from 133 healthy subjects and 99 eyes from 99 early POAG patients were included in the study. The retinal nerve fibre layer （RNFL） thickness parameters of optic nerve head （ONH） and RNFL3.45 scan were measured in one randomly selected eye of each subject using RTVue-100 FD-OCT. Then, we used these parameters to establish the diagnostic models. Four different diagnostic models based on two different area partition strategies on ONH and RNFL3.45 parameters, including ONH traditional area partition model （ONH-T）, ONH new area partition model （ONH-N）, RNFL3.45 traditional area partition model （RNFL3.45-T） and RNFL3.45 new area partition model （RNFL3.45-N）, were built and tested by cross-validation. RESULTS： The new area partition models had higher area under the receiver operating characteristic （AROC; ONH-N： 0.990; RNFL3.45-N： 0.939） than corresponding traditional area partition models （ONH-T： 0.979; RNFL3.45-T： 0.881）. There was no statistical difference among AROC of ONH-T, ONH-N, and RNFL3.45-N. Nevertheless, ONH-N was the simplest model. CONCLUSION： The new area partition models had higher diagnostic accuracy than corresponding traditional area partition models, which can improve the diagnostic ability of early POAG. In particular, the simplest ONH-N diagnostic model may be convenient for clinical application.

关键词： primary open angle glaucoma opticalcoherence tomography ethnic-specific database diagnostic model

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A Clinical Analysis of 293 FUO Patients, A diagnostic model Discriminating infectious Diseases from Non-infectious Diseases

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国际感染病学（电子版） 2014年第2期3卷 54-63页

作者： Qing Zhou Xu-wen Xu De-ming Tan Yu-tao Xie Yun-zhu Long Meng-hou Lu Department of Infectious Disease the Xiangya Hospital of Central-South University Hunan Province Key Laboratory of Viral Hepatitis (Now work in Zhuzhou Central Hospital engaged in the research of infectious diseases) Hunan Province Key Laboratory of Viral Hepatitis Department of Infectious Disease Zhuzhou Central Hospital

Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin(FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases(αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC *** The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease(52.4%), connective tissue diseases(16.8%), neoplastic disease(16.1%) and miscellaneous(2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count(WBC 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11(P ≤ 0.01), respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively(AUC = 0.76, P = 0.00).Conclusions The combination of WBC 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.

关键词： Fever of unknown origin diagnostic model White blood cell Lactate dehydrogenase Lymphadenectasis

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Wavelet-based diagnostic model for Rotating Machinery Subject to Vibration Monitoring

Wavelet-based Diagnostic Model for Rotating Machinery Subjec...

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第二十七届中国控制会议

作者： Pang Peilin,Ding Guangbin Hebei University of Engineering,Handan 056038,P.R.China

This paper proposes a new diagnosis method based on the wavelet transform with fuzzy theory in order to improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of turbo-generator *** increase the signal-noise-ratio,a novel method based on the statistic rule is brought forward to determine the threshold of each order of wavelet space and the decomposition level *** binary discrete wavelet transform is used to acquire effective *** fuzzy diagnosis equation based on correlation matrix is used to classify the fault *** network structure is obtained by establishing the fault diagnosis model of turbo-generator set and using the improved least squares *** the robustness of fault diagnosis equation is discussed in this *** faults are input into the trained diagnosis equation by means of choosing enough samples to train the fault diagnosis equation and the information *** type of fault can be determined according to the output *** experiment results show that multi-concurrent fault for stator temperature fluctuation and rotor vibration can be diagnosed effectively by this new method and the diagnosis result is correct.

关键词： Rotating machinery Vibration monitoring Wavelet transform Fuzzy logic diagnostic model

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Identification Based on Color and Texture of the Soybean Leaf Nitrogen diagnostic model

Identification Based on Color and Texture of the Soybean Lea...

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第二十九届中国控制会议

作者： WANG Lishu College of Engineering,Northeast Agricultural University,Harbin 150030,P.R.China

This paper designed and established gathering diagnosis system model utilized the pattern recognition and the computer vision imagery processing *** on the nitrogen element to the soybean leaf blade to lack,the excessive non-destructive inspection principle and the flow,when the plant lacks the nitrogen,the leaf blade from the leaf margin to the apex etiolution,the etiolution geometrical shape is the V shape *** plant’s nitrogen element excessive,it will cause the color and the texture change.,carried on the sample cultivation,proposed the air zone enhancement and the air zone smooth method carried on the pretreatment to the image,the work is good for the requirements of further processed.

关键词： Pattern Recognition Soybean Nitrogen Element diagnostic model

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Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

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BIOCELL 2023年第6期47卷 1253-1263页

作者： WEI LIANG ZONGWEI ZHANG KEJU YANG HONGTU HU QIANG LUO ANKANG YANG LI CHANG YUANYUAN ZENG Division of Nephrology Renmin Hospital of Wuhan UniversityWuhanChina Nephrology and Urology Research Institute of Wuhan University WuhanChina The First College of Clinical Medical Science Three Gorges UniversityYichangChina School of Life Science and Engineering Southwest Jiaotong UniversityChengduChina

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease *** biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of *** biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary *** machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic ***:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of *** machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic ***:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,***,this GA-BPNN gave better values than the traditional SVM *** diagnosis model may aim for personalized diagnosis and treatment of patients with *** staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were ***:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

关键词： Diabetic nephropathy Renal tubule Machine learning diagnostic model Genetic algorithm

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A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

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Chinese Medical Journal 2023年第9期136卷 1057-1066页

作者： Chengdong Yu Xiaolan Ren Ze Cui Li Pan Hongjun Zhao Jixin Sun Ye Wang Lijun Chang Yajing Cao Huijing He Jin’en Xi Ling Zhang Guangliang Shan Department of Epidemiology and Statistics Institute of Basic Medical SciencesChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China Institute of Chronic and Noncommunicable Disease Control and Prevention Gansu Provincial Centre for Disease Control and PreventionLanzhouGansu 730000China Hebei Provincial Center for Disease Control and Prevention ShijiazhuangHebei 050000China The State Key Lab of Respiratory Disease The First Affiliated HospitalThe School of Public HealthGuangzhou Medical UniversityGuangzhouGuangdong 510182China Department of Epidemiology and Health Statistics School of Public HealthCapital Medical UniversityBeijing 100069China

Background:The prevalence of hypertension is high among Chinese adults,thus,identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention ***:The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017,and they were nonrandomly split into the training set and validation set based on *** logistic regression analysis was performed to develop the diagnostic prediction model,which was presented as a nomogram and a website with risk *** performances of the model were evaluated using discrimination and calibration,and were further compared with a previously published *** curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the ***:The Lasso regression analysis identified the significant predictors of hypertension in the training set,and a diagnostic model was developed using logistic regression.A nomogram with risk classification was constructed to visualize the model,and a website(https://***/hypertension_risk_prediction/)was developed to calculate the exact probabilities of *** model showed good discrimination and calibration,with the C-index of 0.789(95%confidence interval[CI]:0.768,0.810)through internal validation and 0.829(95%CI:0.816,0.842)through external *** curve analysis demonstrated that the model was clinically *** model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China ***:This study developed and validated a diagnostic model for hypertension prediction in Gansu Province.A nomogram and a website were developed to make the model conveniently used to facilitate the indiv

关键词： Hypertension diagnostic model Decision curve Yugur people The China National Health Survey

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Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

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International Journal of Ophthalmology(English edition) 2024年第1期17卷 1-6页

作者： Yu-Ke Ji Rong-Rong Hua Sha Liu Cui-Juan Xie Shao-Chong Zhang Wei-Hua Yang Eye Hospital Nanjing Medical UniversityNanjing 210000Jiangsu ProvinceChina College of Electronic Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210000Jiangsu ProvinceChina Shenzhen Eye Institute Shenzhen Eye HospitalJinan UniversityShenzhen 518000Guangdong ProvinceChina

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of *** the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease *** Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were *** model’s performance was compared to that of the ***:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 *** addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,*** diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was ***:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

关键词： deep learning artificial intelligence Swin Transformer diagnostic model retinal vein occlusion color fundus photographs

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