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Disease spectrum analysis of hospitalized children in china: A study of 18 tertiary children's hospitals

Pediatric Investigation 2019年第3期3卷 159-164页

作者： Guoshuang Feng Yueping Zeng Jian Tian Xinyu Wang Jun Tai Fei Song Xin Zhang Xin Xu Jun Chen Tieliu Shi Xin Ni Big Data and engineering Research center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina beijing Advanced Innovation center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijingChina Medical Record Management Office Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Hospital Administration Office Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Department of Otolaryngology Head and Surgery Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Department of Scientific Research Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Information center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina The center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEastChina Normal UniversityShanghaiChina

Importance: Morbidity and mortality of children are important indicators of the performance of the public health system in any country. In china, the children's disease spectrum has gradually changed in recent years. However, the gender- and age-specific disease spectrum for hospitalized children under 15 years old is still unclear. Objective: To explore the gender- and age-based distribution of diseases in hospitalized children under 15 years in china. Methods: Medical records home page data for 2.16 to 2.18 were collected from 18 tertiary children's hospitals in china. The gender- and age-specific disease spectrum was analyzed, using the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Results: The most common diseases were those of the respiratory system (2..7% of all 2.32.42.hospitalized children). The top three diseases for boys were diseases of the respiratory system (2..6%), diseases of the digestive system (11.4%) and certain conditions originating in the perinatal period (8.6%). The top three diseases for girls were diseases of the respiratory system (2..9%), certain conditions originating in the perinatal period (10.1%), and factors influencing health status and contact with health services (9.4%).The most common diseases for children under 1 year old were certain conditions originating in the perinatal period (38.1%). For all other age groups, the most common conditions were respiratory diseases (33.8% for those aged 1–3 years, 2..2. for those aged 4–6 years, and 12.2. for those aged 7–14 years). Interpretation: This study analyzed the medical records home pages of 18 children's hospitals to provide the first overview of the disease spectrum and its gender- and age-specific distribution among children in china.

关键词： Disease spectrum Hospitalized children china

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Chinese guideline for the application of rectal cancer staging recognition systems based on artificial intelligence platforms (2.2. edition)

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Chinese Medical Journal 2021年第11期134卷 1261-1263页

作者： Yuan Gao Yun Lu Shuai Li Yong Dai Bo Feng Fang-Hai Han Jia-Gang Han Jing-Jing He Xin-Xiang Li Guo-Le Lin Qian Liu Gui-Ying Wang Quan Wang Zhen-Ning Wang Zheng Wang Ai-Wen Wu Bin Wu Ying-Chi Yang Hong-Wei Yao Wei Zhang Jian-Ping Zhou Ai-Min Hao Zhong-Tao Zhang Colorectal Surgery Group of the Surgery Branch in the Chinese Medical Association Beihang university State Key Laboratory of Virtual Reality Technology and Systems Department of General Surgery The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266555China Beihang university State Key Laboratory of Virtual Reality Technology and Systems Beijing 100191China Department of General Surgery Qilu Hospital of Shandong UniversityJinanShandong 250012China Department of General Surgery Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Shanghai Minimally Invasive Surgery center Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Department of Gastrointestinal Surgery Sun Yat-sen UniversityGuangzhouGuangdong 510120China Department of General Surgery Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100020China Institute of International Law Chinese Academy of Social SciencesBeijing 100732China Department of Colorectal Surgery Fudan University Shanghai Cancer CenterShanghai 201321China Department of Oncology Shanghai Medical CollegeFudan UniversityShanghai 201321China Department of General Surgery Peking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijing 100730China National Cancer center/National Clinical Research center for Cancer/Cancer Hospital Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Third Hospital of Hebei Medical university ShijiazhuangHebei 050051China Department of Colon and Rectum Surgery The First Hospital of Jilin UniversityChangchunJilin 130021China Department of Surgical Oncology and General Surgery Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal TumorsMinistry of EducationThe First Affiliated Hospital of China Medical UniversityShenyangLiaoning 110001China Department of Gastrointestinal Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei 430022China Department of Unit III&Ostomy Service Key laboratory of Carcinogenesis and Translational Research(Ministry of Education)Gastrointest

Development and Application of Artificial Intelligence Recognition Systems in Rectal Cancer Staging Whether for surgical treatment or for neoadjuvant chemoradiotherapy,imaging evaluation has become an important basis to perform the treatment plans.[1]The reading of imaging results requires a large number of experienced radiologists to complete,but shortages and uneven distributions of personnel cause delays and biases in imaging ***,independent research and development of automatic recognition systems of rectal cancer staging based on artificial intelligence(AI)platforms aim to partially replace practitioners’work and achieve rapid and accurate identification of rectal cancer staging.

关键词： staging artificial replace

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糖尿病患者颈动脉斑块磁共振成像特征与急性脑缺血病灶形态关系研究

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中国卒中杂志 2018年第1期13卷 46-53页

作者：孙贝贝李晓刘晓晟赵锡海许建荣赵辉林上海交通大学医学院附属仁济医院放射科上海200127 清华大学医学院生物医学影像研究中心

目的探讨2.糖尿病患者颈动脉斑块磁共振成像(magnetic resonance imaging,MRI)特征与相应供血区急性脑缺血病灶形态的关系。方法横断面研究,入组临床急性颈内动脉供血区缺血性卒中患者,排除心源性卒中,在发病一周内行颈动脉磁共振管壁... 详细信息

目的探讨2.糖尿病患者颈动脉斑块磁共振成像(magnetic resonance imaging,MRI)特征与相应供血区急性脑缺血病灶形态的关系。方法横断面研究,入组临床急性颈内动脉供血区缺血性卒中患者,排除心源性卒中,在发病一周内行颈动脉磁共振管壁成像(magnetic resonance vessel wall imaging,MR-VWI)检查和常规MRI[包括磁共振血管成像(magnetic resonance angiography,MRA)和弥散加权成像(diffusion-weighted imaging,DWI)]检查,评估症状侧颈动脉斑块负荷和成分特征以及颅内外动脉管腔狭窄度,根据DWI上梗死灶形态对急性脑缺血病灶形态和体积进行分析。结果共入选140例急性缺血性卒中患者,其中68例(48.6%)为2.糖尿病患者,多因素回归分析显示2.糖尿病是症状侧颈动脉斑块富脂质核发生的独立危险因素[比值比(odds ratio,OR)3.35,95%可信区间(confidence interval,CI)1.33~8.43];在兼具富脂质核斑块的患者中,2.糖尿病组颈内动脉区域急性脑缺血病灶体积[(15.45±8.97)ml vs(9.09±8.64)ml,P=0.011]和大穿支动脉梗死发生率[34.0%vs 13.2.,P=0.02.)]显著高于非糖尿病组。结论颈动脉供血区急性缺血性卒中患者中,2.糖尿病与症状侧颈动脉动脉粥样硬化斑块MRI特征,尤其是富脂质核密切相关,且更易发生大穿支动脉的梗死,提示MR-VWI细致化分析结合糖尿病因素有助于临床对缺血性卒中风险的分层和个体化诊治。

关键词：糖尿病缺血性卒中颈动脉斑块富脂质核磁共振成像管壁成像

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china Patient-centered Evaluative Assessment of Cardiac Events Prospective Study of Acute Myocardial Infarction： Study Design

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Chinese Medical Journal 2016年第1期129卷 72-80页

作者： Rachel P Dreyer Xi Li Xue Du Nicholas S Downing Li Li Hai-Bo Zhang Fang Feng Wen-Chi Guan Xiao Xu Shu-Xia Li Zhen-Qiu Lin Frederick A Masoudi John A Spertus Harlan M Krumholz Li-Xin Jiang National Clinical Research center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100037 China center for Outcomes Research and Evaluation Yale-New Haven Hospital New Haven Connecticut USA Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven Connecticut USA Department of Obstetrics Gynecology and Reproductive Sciences Yale School of Medicine New Haven Connecticut USA Division of Cardiology University of Colorado Anschutz Medical Campus Aurora Colorado USA Department of biomedical and Health Informatics University of Missouri - Kansas City Kansas City Missouri USA Saint Luke's Mid America Heart Institute Kansas City Missouri USA The Robert Wood Johnson Foundation Clinical Scholars Program Department of Internal Medicine Yale School of Medicine New Haven Connecticut USA Department of Health Policy and Management Yale School of Public Health New Haven Connecticut USA

Background： Despite the rapid growth in the incidence of acute myocardial infarction （AMI） in china, there is limited information about patients＇ experiences after AMI hospitalization, especially on long-tern1 adverse events and patient-reported outcomes （PROs）. Methods： The china Patient-{2.ed Evaluative Assessment of Cardiac Events （PEACE）-Prospective AMI Study will enroll 4000 consecutive AM I patients from 53 diverse hospitals across china and follow them longitudinally for 12.months to docunlent their treatment, recovery, and outcomes. Details of patients＇ medical history, treatment, and in-hospital outcomes are abstracted from medical charts. Comprehensive baseline interviews are being conducted to characterize patient demographics, risk factors, presentation, and healthcare utilization. As part of these interviews, validated instruments are administered to measure PROs, including quality of life, symptoms, mood, cognition, and sexual activity. Follow-up interviews, measuring PROs, medication adherence, risk factor control, and collecting hospitalization events are conducted at 1, 6, and 12.months after discharge. Supporting documents for potential outcomes are collected for adjudication by clinicians at the National Coordinating {2.. Blood and urine samples are also obtained at baseline, 1 - and 12.month follow-up. In addition, we are conducting a survey of participating hospitals to characterize their organizational characteristics. Conclusion： The china PEACE-Prospective AMI study will be uniquely positioned to generate new information regarding patient＇s experiences and outcomes alter AMI in china and seiwe as a foundation for quality improveinent activities.

关键词： Acute Myocardial Infarction Outcomes Research Patient-reported Outcome Measures Prospective Cohort

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Chinese guidelines for the application of colon cancer staging recognition systems based on artificial intelligence platforms (2.2.)

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Intelligent Medicine 2021年第1期1卷 37-42页

作者： Yun Lu Shuai Li Yuan Gao Yong Dai Bo Feng Fanghai Han Jiagang Han Jingjing He Xinxiang Li Guole Lin Qian Liu Guiying Wang Quan Wang Zhenning Wang Zheng Wang Aiwen Wu Bin Wu Yingchi Yang Hongwei Yao Wei Zhang Jianping Zhou Aimin Hao Zhongtao Zhang 无 Department of General Surgery The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China Beihang university State Key Laboratory of Virtual Reality Technology and Systems Beijing 100191China Department of General Surgery Qilu Hospital of Shandong UniversityJinanShandong 250012China Department of General Surgery Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200020China Shanghai Minimally Invasive Surgery center Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200020China Department of Gastrointestinal Surgery Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhouGuangdong 510120China Department of General Surgery Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100027China Institute of International Law Chinese Academy of Social SciencesBeijing 102442China Department of Colorectal Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Department of Oncology Shanghai Medical CollegeFudan UniversityShanghai 200032China Department of General Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China National Cancer center/National Clinical Research center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Third Hospital of Hebei Medical university ShijiazhuangHebei 50051China Department of Colon and Rectum Surgery The First Hospital of Jilin UniversityChangchunJilin 130031China Department of Surgical Oncology and General Surgery Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal TumorsMinistry of EducationThe First Affiliated Hospital of China Medical UniversityShenyangLiaoning 110001China Department of Gastrointestinal Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei 430030China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Department of Unit II

The incidence and mortality of colon cancer in china are increasing each *** present,treatment selection for colon cancer patients mainly depends on imaging results,which require a large number of radiologists to *** china,there is a shortage and uneven distribution of experienced radiologists,which leads to delays and bias in the evaluation of imaging *** on these considerations,the Colorectal Surgery Group of the Surgery Branch of the Chinese Medical Association in collaboration with experts at Beihang {2. has independently developed an artificial intelligence(AI)-based recognition system for the preoperative determination of colon cancer stage to partially replace the work of and relieve the pressure on *** guidelines aim to standardize the use of AI-based recognition systems in the preoperative staging of colon cancer and guide their clinical application.

关键词： Artificial intelligence Colon cancer Deep learning Preoperative staging

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