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    吕军

    发布时间:2021-08-16发布人:护理学院浏览量:3569

    护理学院硕士研究生导师简介

    姓名  吕军

    职称  研究员

    导师类型  科学学位硕士生导师

    一、受教育简历

        19969-20017月,西安交通大学医学院,医学学士学位

        20019-200612月,西安交通大学医学院,医学博士学位

    二、工作简历

        20081-20106月,第四军医大学药学系药理学教研室,博士后

        20107-20149月,西安交通大学第一附属医院药学部,临床药师

        20149-20201月,西安交通大学第一附属医院临床研究中心,副主任

        20201月至今,暨南大学附属第一医院临床研究部,主任

    三、任教课程

        《流行病学》、《医学统计学》、《临床数据挖掘与运用》、《医学科研设计与论文写作》

    四、研究方向

        重症护理、心脑血管疾病临床研究、临床大数据挖掘

    五、主持及参与的主要课题

    1.探索药物基因组学检测项目临床准入模式的研究. 主持. 国家社会科学基金一般项目(编号:16BGL183),20万元. 2017.1-2021.6.

    2.交感神经内共存递质囊泡循环调控的分子机理研究. 主持. 国家自然科学基金青年科学基金项目(编号:30800310),18万元. 2009.1-2011.12.

    3.腺苷合成、代谢酶与受体的基因多态性对慢性心力衰竭的影响. 主持. 陕西省自然科学基础研究计划-面上项目(编号:2015JM8415),3万元. 2015.1-2016.12.


    六、近年发表的主要论文(通讯作者或共同通讯作者)

    (一)MIMIC数据库论文

    1.  Effect of first trough vancomycin concentration on the occurrence of AKI in critically ill patients: A retrospective study of the MIMIC-IV database. Frontiers in Medicine 2022, Accepted. Journal impact factor: 5.091.

    2. Antithrombotic therapy improves ICU mortality of septic patients with peripheral vascular disease. International Journal of Clinical Practice 2022, 2022: 1288535. Journal impact factor: 2.503.

    3. Association Between Blood Pressure During Vasopressor Weaning and Hospital Survival: What are the Optimal Targets of Vasopressor Support? Emergencia 2022, Accepted. Journal impact factor: 3.881.

    4. The Association Between Continuous Renal Replacement Therapy as Treatment for Sepsis-Associated Acute Kidney Injury and Trend of Lactate trajectory as Risk Factor of 28-Day Mortality in Intensive Care Units. BMC Emergency Medicine 2022, 22: 32. Journal impact factor: 2.119.

    5. Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest). BMC Emergency Medicine 2022, 22: 16. Journal impact factor: 2.119.

    6. A novel risk-prediction scoring system for sepsis among patients with acute pancreatitis: a retrospective analysis of a large clinical database. International Journal of Clinical Practice 2022, 2022: 5435656. Journal impact factor: 2.503.

    7. Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression. Frontiers in Cardiovascular Medicine 2022, 9: 847206. Journal impact factor: 6.05.

    8. Influence of ambulatory blood pressure-related indicators within 24 h on in-hospital death in sepsis patients. International Journal of Medical Sciences, 2022, 19(3): 460-471. Journal impact factor: 3.738.

    9. Risk factor analysis and Nomogram for predicting In-Hospital Mortality in ICU patients with sepsis and lung infection. BMC Pulmonary Medicine 2022, 22: 17. Journal impact factor: 3.317.

    10. Analysis of the correlation between the longitudinal change trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling. Journal of Intensive Medicine 2022, 2(1): 39-49.

    11. Influence of the trajectory of the urine output for 24 hours on the occurrence of AKI in patients with sepsis in intensive care unit. Journal of Translational Medicine 2021, 19: 518. Journal impact factor: 5.531.

    12. Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units. Computational and Mathematical Methods in Medicine 2021, https://doi.org/10.1155/2021/5745304. Journal impact factor: 2.238.

    13. Developing and verifying a multivariate model to predict the survival probability after coronary artery bypass grafting in patients with coronary atherosclerosis based on the MIMIC-III database. Heart & Lung, 2021, 52: 61-70. Journal impact factor: 2.21.

    14. Obesity paradox of all-cause mortality in 4133 patients treated with coronary revascularization. Journal of Interventional Cardiology 2021, 3867735. Journal impact factor: 2.279.

    15. Influence of fluid balance on the prognosis of patients with sepsis. BMC Anesthesiology 2021, 21(1): 269. Journal impact factor: 2.217.

    16. A new scoring system for predicting in-hospital death in patients having liver cirrhosis with esophageal varices. Frontiers in Medicine, 2021, 8: 678646. Journal impact factor: 5.091.

    17. A novel nomogram for predicting survival in patients with severe acute pancreatitis: an analysis based on the large MIMIC-III clinical database. Emergency Medicine International 2021, https://doi.org/10.1155/2021/9190908. Journal impact factor: 1.112.

    18. Establishment of a prognostic model for patients with sepsis based on SOFA: a retrospective cohort study. Journal of International Medical Research 2021, 49(9): 3000605211044892. Journal impact factor: 1.671.

    19. Using restricted cubic splines to study the trajectory of systolic blood pressure in the prognosis of acute myocardial infarction. Frontiers in Cardiovascular Medicine 2021, 8: 740580. Journal impact factor: 6.05.

    20. The role of glucocorticoids in the treatment of ARDS: a multicenter retrospective study based on the eICU Collaborative Research Database. Frontiers in Medicine 2021, 8: 678260. Journal impact factor: 5.091.

    21. Prognostic Value of Blood Urea Nitrogen/Creatinine Ratio for Septic Shock: An Analysis of the MIMIC-III Clinical Database. BioMed Research International 2021, https://doi.org/10.1155/2021/5595042. Journal impact factor: 3.411.

    22. Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection. Frontiers in Medicine 2021, 8: 671184. Journal impact factor: 5.091.

    23. Effects of stress hyperglycemia on short-term prognosis of patients without diabetes mellitus in Coronary Care Unit. Frontiers in Cardiovascular Medicine 2021, https://doi.org/10.3389/fcvm.2021.683932. Journal impact factor: 6.05.

    24. Exploration and Establishment A Prognostic Model Based on The SOFA Score for First Diagnosed Acute Myocardial Infarction Patients. Journal of International Medical Research 2021, 49(5): 1-15. Journal impact factor: 1.671.

    25.  Body Mass Index Linked to Short-Term and Long-Term All-Cause Mortality in Patients with Acute Myocardial Infarction. Postgraduate Medical Journal 2021, http://dx.doi.org/10.1136/postgradmedj-2020-139677. Journal impact factor: 2.401.

    26. A nomogram for predicting the risk of sepsis in patients with acute cholangitis. Journal of International Medical Research 2019, August 20. doi: 10.1177/0300060519866100. Journal impact factor: 1.287.

    27. Description of clinical characteristics of VAP patients in MIMIC database. Frontiers in Pharmacology 2019, 10: 62. Journal impact factor: 4.225.

    28. Red cell distribution width to platelet ratio is associated with increasing in-hospital mortality in critically ill patients with acute kidney injury. Disease Markers 2022, Accepted. Journal impact factor: 3.434

    29. 基于MATLAB的医学影像数据迁移学习的实现. 医学新知, 2022, 32(01): 33-39.

    30. 多变量选择方法在临床预测模型中的验证:基于MIMIC数据库. 中国循证医学杂志, 2021, 21(12): 1463-1467.


    (二)SEER数据库论文:

    1. Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a Surveillance, Epidemiology, and End Results analysis. BMC Cancer 2022, 22: 210. Journal impact factor: 4.43.

    2. How socioeconomic and clinical factors impact prostate-cancer-specific and other-cause mortality in prostate cancer stratified by clinical stage: competing-risk analysis. Prostate 2021, https://doi.org/10.1002/pros.24287. Journal impact factor: 4.104.

    3. Midlife Brain Metastases in the United States: Is Male at Risk? Cancer Medicine, 2022, https://doi.org/10.1002/cam4.4499. Journal impact factor: 4.452.

    4. Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population-based Study. International Journal of General Medicine 2021, 14: 9841-9851. Journal impact factor: 2.466.

    5. Examining more lymph nodes may improve the prognosis of patients with right colon cancer: determining the optimal minimum lymph node count. Cancer Control 2021, 28: 1-14. Journal impact factor: 3.302.

    6. Evaluation and prediction analysis of 3- and 5-year survival rates of patients with cecal adenocarcinoma based on period analysis. International Journal of General Medicine 2021, 14: 7317-7327. Journal impact factor: 2.466.

    7. Socioeconomic status and adult gliomas mortality risk: An observational study based on SEER data. World Neurosurgery 2021, DOI: 10.1016/j.wneu.2021.08.034. Journal impact factor: 2.104.

    8. Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: a SEER-based study. Cancer Control 2021, 28: 10732748211036775. Journal impact factor: 3.302.

    9. Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis. BMJ open 2021, 11: e048243. Journal impact factor: 2.692.

    10. Nomograms for Differentiated Thyroid Carcinoma Patients Based on the Eighth AJCC Staging and Competing-Risks Model. JNCI Cancer Spectrum 2021, 5(3): pkab038. Journal impact factor: 未获得.

    11. Nomograms for estimating cause-specific death rates of patients with inflammatory breast cancer: a competing-risks analysis. Technology in Cancer Research & Treatment, 2021, 20: 1-12. Journal impact factor: 3.399.

    12. Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: a population-based analysis. Cancer Medicine 2021, 10(11): 3756-3769. Journal impact factor: 4.452.

    13. Competing-risks nomograms for predicting the prognosis of patients with infiltrating lobular carcinoma of the breast. Clinical Breast Cancer 2021, https://doi.org/10.1016/j.clbc.2021.03.008. Journal impact factor: 3.225.

    14. Coincident patterns of suicide risk among adult patients with a primary solid tumor: a large-scale population study. International Journal of General Medicine 2021, 14: 1107-1119. Journal impact factor: 2.466.

    15. Prognostic exploration of all-cause death in gingival squamous cell carcinoma: a retrospective analysis of 2076 patients. Journal of Oncology 2021, https://doi.org/10.1155/2021/6676587. Journal impact factor: 4.375.

    16.  Competitive risk analysis of prognosis in patients with cecum cancer: a populationbased study. Cancer Control 2021, https://doi.org/10.1177/1073274821989316. Journal impact factor: 3.302.

    17.  A novel nomogram based on a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma. Annals of Translational Medicine 2021, 9(5): 378. Journal impact factor: 3.932.

    18.  A nomogram for determining the disease-specific survival in invasive lobular carcinoma of the breast: a population study. Medicine 2020, 99(43): e22807. Journal impact factor: 1.889.

    19.  Risk factors associated with suicide among leukemia patients: a Surveillance, Epidemiology, and End Results analysis. Cancer Medicine 2020, 9: 9009-9017. Journal impact factor: 4.452.

    20.  Prognostic factors in patients with rhabdomyosarcoma using competing-risks analysis: a study of cases in the SEER database. Journal of Oncology 2020, https://doi.org/10.1155/2020/2635486. Journal impact factor: 4.375.

    21.  Establishment and validation of a nomogram for tonsil squamous cell carcinoma: a retrospective study based on the SEER database. Cancer Control 2020, 27(1):1073274820960481. Journal impact factor: 3.302.

    22.  Prognostic factors in patients with gallbladder adenocarcinoma identified using competing-risks analysis: a study of cases in the SEER database. Medicine 2020, 99(31): e21322. Journal impact factor: 1.889.

    23. Competing-risks model for predicting the postoperative prognosis of patients with papillary thyroid adenocarcinoma based on the SEER database. Medical Science Monitor 2020,26:e924045. DOI: 10.12659/MSM.924045. Journal impact factor: 2.649.

    24. A Prognostic Nomogram for the Cancer-Specific Survival of Patients with Upper-Tract Urothelial Carcinoma Based on the Surveillance, Epidemiology, and End Results Database. BMC Cancer 2020, 20: 534. Journal impact factor: 4.43.

    25. A Prognostic Nomogram for Pancreatic Ductal Adenocarcinoma Patients’ All-cause Survival in a Surveillance, Epidemiology, and End Results Analysis. Translational Cancer Research 2020, 9(5):3586-3599. Journal impact factor: 1.241.

    26. Incidence of and sociological risk factors for suicide death in patients with leukemia: a population-based study. Journal of International Medical Research 2020, DOI: 10.1177/0300060520922463. Journal impact factor: 1.671.

    27. Nomogram predicting cancer-specific mortality in early-onset rectal cancer: A competing risk analysis. International Journal of Colorectal Disease 2020, https://doi.org/10.1007/s00384-020-03527-9. Journal impact factor: 2.571.

    28.  Development and validation of a nomogram for predicting long-term overall survival in nasopharyngeal carcinoma: A population-based study. Medicine 2020, 99(4):e18974. Journal impact factor: 1.889.

    29. Prognostic factors and survival outcomes according to tumor subtype in patients with breast cancer lung metastases. PeerJ 2019, https://peerj.com/articles/8298/. Journal impact factor: 2.379.

    30. Comparison of survival outcomes in medullary carcinoma and invasive ductal carcinoma of the breast. Future Oncology 2019, 15(27): 3111-3123. Journal impact factor: 2.66

    31. Competing-risks model for predicting the prognosis of penile cancer based on the SEER database. Cancer Medicine 2019, 8: 7881-7889. Journal impact factor: 3.491.

    32. Insurance Status is Related to Overall Survival in Patients with Small-Intestine Adenocarcinoma: A Population-Based Study. Current Problems in Cancer 2019, Sep 17. doi: 10.1016/j.currproblcancer.2019.100505. Journal impact factor: 3.264

    33. Development and validation of a nomogram for predicting cancer-specific survival in patients with Wilms’ tumor. Journal of Cancer 2019, 10(21): 5299-5305. Journal impact factor: 3.565.

    34. A nomogram for predicting the survival of patients with malignant melanoma: a population analysis. Oncology Letters 2019, 18(4): 3591-3598. Journal impact factor: 2.311.

    35. Nomograms for predicting the survival rate for cervical cancer patients who undergo radiation therapy: a SEER analysis. Future Oncology 2019, 15(26): 3033-3045. Journal impact factor: 2.66.

    36. Prognostic factors in patients with gastric adenocarcinoma using competing-risk analysis: a study of cases in the SEER database. Scandinavian Journal of Gastroenterology 2019, 54(8): 1015-1021. Journal impact factor: 2.13.

    37. Development and Validation of a Nomogram for Predicting Survival in Patients with Thyroid cancer. Medical Science Monitor 2019, 25: 5561-5571. Journal impact factor: 1.918.

    38. A Nomogram for Determining the Disease-Specific Survival in Ewing Sarcoma: a Population Study. BMC Cancer 2019, 19: 667-675. Journal impact factor: 3.15.

    39. Effect of marital status on duodenal adenocarcinoma survival: A Surveillance Epidemiology and End Results population analysis. Oncology Letters 2019, 18: 1904-1914. Journal impact factor: 2.311.

    40. Development and Validation of a Nomogram Containing the Prognostic Determinants of Chondrosarcoma Based on the Surveillance, Epidemiology, and End Results Database. International Journal of Clinical Oncology 2019, 24: 1459-1467. Journal impact factor: 2.879.

    41. Nomograms for predicting long-term overall survival and cancer-specific survival in lip squamous cell carcinoma: a population-based study. Cancer Medicine 2019, 8(8): 4032-4042. Journal impact factor: 3.491.

    42. A nomogram for predicting survival in patients with nodular melanoma: A Population-based Study. Medicine 2019, 98(24): e16059. Journal impact factor: 1.552.

    43. Development and Validation of A Nomogram for Osteosarcoma-specific Survival: A Population-based Study. Medicine 2019, 98(23): e15988. Journal impact factor: 1.552.

    44. Development and validation of a nomogram for predicting survival in male patients with breast cancer. Frontiers in Oncology 2019, 9: 361. Journal impact factor: 4.848.

    45. Incidence and risk factors for suicide death in male patients with genital-system cancer in the United States. European Journal of Surgical Oncology 2019, 45: 1969-1976. Journal impact factor: 3.959.

    46. Determining the optimal cutoff point for lymph node density and its impact on overall survival in children with Wilms’ tumor. Cancer Management and Research 2019, 11: 759-766. Journal impact factor: 2.886.

    47. Incidence rate and risk factors for suicide death in patients with skin malignant melanoma: a Surveillance, Epidemiology, and End Results analysis. Melanoma Research 2018 Nov 26. doi: 10.1097/CMR.0000000000000559. Journal impact factor: 2.381

    48. The impact of the lymph node density on overall survival in patients with Wilms’ tumor: a SEER analysis. Cancer Management and Research 2018, 10: 671-677. Journal impact factor: 2.243


    (三)GBD数据库论文:

    1. Epidemiological trends of tracheal, bronchus, and lung cancer at the global, regional, and national levels: a population-based study. Journal of Hematology & Oncology 2020, 13: 98. Journal impact factor: 17.388

    2. Global, Regional, and National Burden of Hodgkin Lymphoma from 1990 to 2017: Estimates from the 2017 Global Burden of Disease study. Journal of Hematology & Oncology 2019, 12: 107. Journal impact factor: 11.059

    3.  Global, Regional, and National Burdens of Oral Cancer, 1990 to 2017: Results from the Global Burden of Disease Study. Cancer Communications 2020, 40: 81-92. Journal impact factor: 10.392

    4. Global Burden of Thyroid Cancer From 1990-2017. JAMA Network Open 2020, 3(6): e208759. Journal impact factor: 8.483

    5. Secular trends in the incidence of eating disorders in China from 1990 to 2017: a joinpoint and age–period–cohort analysis. Psychological Medicine 2020, https://doi.org/10.1017/S0033291720002706. Journal impact factor: 7.723

    6. Trends of the incidence of drug use disorders from 1990 to 2017: an analysis based on the Global Burden of Disease 2017 data. Epidemiology and Psychiatric Sciences 2020, 29: e148. Journal impact factor: 6.892

    7. Trends in the incidence and DALYs of schizophrenia at the global, regional and national levels: results from the Global Burden of Disease Study 2017. Epidemiology and Psychiatric Sciences 2020, 29: e91. Journal impact factor: 6.892

    8. Dietary risk related colorectal cancer burden: Estimates from 1990 to 2019. Frontiers in Nutrition 2021, 8: 690663. Journal impact factor: 6.576.

    9. Global Burden of Larynx Cancer, 1990-2017: Estimates from the Global Burden of Disease 2017 Study. Aging-US 2020, 12: https://doi.org/10.18632/aging.102762. Journal impact factor: 5.682

    10. He HR, Liang L, Han DD, Xu FS, Lyu J(通讯作者). Different trends in the incidence and mortality rates of prostate cancer between China and the USA: a joinpoint and age–period–cohort analysis. Frontier in Medicine 2022, 9: 824464. Journal impact factor: 5.091.

    11. Trends in the incidence and DALYs of bipolar disorder at global, regional, and national levels: results from the Global Burden of Disease Study 2017. Journal of Psychiatric Research 2020, 125: 96-105. Journal impact factor: 4.791.

    12. Changes in the global burden of depression from 1990 to 2017: findings from the Global Burden of Disease study. Journal of Psychiatric Research 2020, 126: 134-140. Journal impact factor: 4.791.

    13.  He HR, Pan ZY, Wu JY, Hu CY, Bai L, Lyu J(通讯作者). Health effects of tobacco at the global, regional, and national levels: results from the 2019 Global Burden of Disease study. Nicotine & Tobacco Research 2021, https://doi.org/10.1093/ntr/ntab265. Journal impact factor: 4.244.

    14. Changing epidemiology of chronic kidney disease due to type 2 diabetes mellitus from 1990 to 2017: estimates from GBD 2017. Journal of Diabetes Investigation 2020, DOI:10.1111/jdi.13355. Journal impact factor: 4.232

    15. The gap between global tuberculosis incidence and the first milestone of the WHO End Tuberculosis Strategy: An analysis based on the Global Burden of Disease 2017 database. Infection and Drug Resistance 2020, 13: 1281-1286. Journal impact factor: 4.003.

    16. Trends in the incidence of diabetes mellitus: results from the Global Burden of Disease Study 2017 and implications for diabetes mellitus prevention. BMC Public Health 2020, 20: 1415. Journal impact factor: 3.295.

    17. Global, regional, and national burdens of bladder cancer in 2017: estimates from the 2017 Global Burden of Disease study. BMC Public Health 2020, 20: 1963. Journal impact factor: 3.295.

    18. Global, Regional, and National Burden of Nasopharyngeal Carcinoma from 1990 to 2017: Results from the Global Burden of Disease Study 2017. Head & Neck 2020, http://dx.doi.org/10.1002/hed.26378. Journal impact factor: 3.147.

    19. Global, regional, and national disability-adjusted life years due to HIV from 1990 to 2019: findings from the Global Burden of Disease Study 2019. Tropical Medicine & International Health 2021, doi:10.1111/tmi.13565. Journal impact factor: 2.308

    20. 1990—2019年中国霍奇金淋巴瘤流行趋势和疾病负担分析. 医学新知, 2021, 31(06):433-440.

    21.  1990年至2017年口腔癌的全球和区域负担:疾病全球负担研究报告. 癌症, 2020, 39(4): 159-171.


    (四)NHANES数据库论文:

    1. Relationship between self-reported sleep duration during week-/work-days and metabolic syndrome from NHANES 2013–2016. Sleep and Breathing 2021, https://doi.org/10.1007/s11325-021-02522-w. Journal impact factor: 2.816.

    2.  Dose-response association of Waist-to-Height Ratio Plus BMI and risk of depression: evidence from the NHANES 05-16. International Journal of General Medicine 2021, 14: 1283-1291. Journal impact factor: 2.466.

    3.  Association Between Physical Activity and Kidney Stones Based on Dose–Response Analyses Using Restricted Cubic Splines. European Journal of Public Health 2020, 30(6): 1206-1211. Journal impact factor: 3.367.

    4.  Relationship between body mass index and kidney stones based on dose–response analyses using restricted cubic splines applied to NHANES 2011–2016 data. Journal of Renal Nutrition 2020, 11(6): 1303-1316. Journal impact factor: 3.655.

    5. Physical Activity Levels and Diabetes Prevalence in US Adults: Findings from NHANES 2015-2016. Diabetes Therapy 2020, DOI: https://doi.org/10.1007/s13300-020-00817-x. Journal impact factor:2.945.


    (五)CHNS数据库论文:

    1. Temporal Trends in Food Preferences and Their Association with Overweight/Obesity Among Children in China. International Journal of Gastronomy and Food Science 2021, https://doi.org/10.1016/j.ijgfs.2021.100335. Journal impact factor: 2.537.

    2. Forecasting the populations of overweight and obese Chinese adults. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2020, 13: 4849-4857. Journal impact factor: 3.168.

    3. Association between alcohol consumption and hypertension in Chinese adults: findings from the CHNS. ALCOHOL 2020, 83: 83-88. Journal impact factor: 2.405.

    4. Longitudinal study of the relationship between sleep duration and hypertension in Chinese adult residents (CHNS 2004–2011). Sleep Medicine 2019, 58: 88-92. Journal impact factor: 3.038.


    (六)临床大数据挖掘类SCI综述:

    1. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Military Medical Research 2021, 8: 44.

    2. Brief introduction of medical database and data mining technology in big data era. Journal of Evidence-Based Medicine 2020, 13: 57-69.


    (七)指导本科生发表论文:

    1.  Evaluation and prediction analysis of 3- and 5-year survival rates of patients with cecal adenocarcinoma based on period analysis. International Journal of General Medicine 2021, 14: 7317-7327. Journal impact factor: 2.466.

    2. Prognostic factors in patients with gastric adenocarcinoma using competing-risk analysis: a study of cases in the SEER database. Scandinavian Journal of Gastroenterology 2019, 54(8): 1015-1021. Journal impact factor: 2.13.

    3. Insurance Status is Related to Overall Survival in Patients with Small-Intestine Adenocarcinoma: A Population-Based Study. Current Problems in Cancer 2019, Sep 17. doi: 10.1016/j.currproblcancer.2019.100505. Journal impact factor: 3.264

    4.  Effect of marital status on duodenal adenocarcinoma survival: A Surveillance Epidemiology and End Results population analysis. Oncology Letters 2019, 18: 1904-1914. Journal impact factor: 2.311.

    5.  The association between XRCC1 Arg399Gln polymorphism and risk of leukemia in different populations: a meta-analysis of case-control studies. OncoTargets and Therapy 2015, 8: 3277–3287. IF: 2.339.

    6. Cyclin D1 G870A gene polymorphism and risk of leukemia and hepatocellular carcinoma: a meta-analysis. Genetics and Molecular research, 2015, 14(2): 5171-5180. Journal impact factor: 0.764.


    七、重要社会任职(兼职)

    • 中国医疗保健国际交流促进会循证医学分会临床研究学组,组长

    • 中华医学会临床流行病学和循证医学分会循证医学学组,委员

    • 中国医药教育协会医药统计专业委员会,委员

    • 中国康复医学会循证康复医学委员会,副主任委员

    • 中国优生优育协会孕产妇与儿童创伤专业委员会,副主任委员

    • 《中国循证医学杂志》第七届编委会,委员

    • 《中国循证心血管医学杂志》第三届编委会,委员

    • 中国医疗保健国际交流促进会循证医学分会,委员

    • 广东省护士协会大数据管理护士分会,会长

    • 广东省医学会临床研究学分会,委员

    • 广东省医学会循证医学分会,委员