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      广州市黄埔大道西601号 暨南大学护理学院

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      85228085

    吕军

    发布时间：2024-07-10发布人：护理学院浏览量：4316

    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

    • 1996年9月-2001年7月，西安交通大学医学院，医学学士学位；

    • 2001年9月-2006年12月，西安交通大学医学院，医学博士学位


    二、工作简历

    • 2008年1月-2010年6月，第四军医大学药学系药理学教研室，博士后；

    • 2010年7月-2014年9月，西安交通大学第一附属医院药学部，临床药师；

    • 2014年9月-2020年1月，西安交通大学第一附属医院临床研究中心，副主任；

    • 2020年1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

    • 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.

    • 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.

    • 基于机器学习和深度学习的脓毒症预警模型的建立与临床应用. 主持. 广州市基础研究计划2023年度市校（院）企联合资助专题（编号：2023A03J1032），20万元. 2023.4-2025.3.

    • 基于机器学习和深度学习的脓毒症预警模型的建立与临床验证研究. 主持. 广州市基础研究计划市校（院）联合资助基础与应用基础研究项目（编号：202201020054），20万元. 2022.4-2024.3.

    • 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）

    • MIMIC数据库论文

    1. Association of magnesium sulfate use with mortality in critically ill patients with sepsis: a retrospective propensity score-matched cohort study. British Journal of Anaesthesia 2023, 131(5): 861-870. IF: 9.80.一区. Top.

    2. Pre-intensive care unit use of selective serotonin reuptake inhibitors and mortality in critically ill adults with mental disorders: analysis from the MIMIC-IV database. Translational Psychiatry 2023, 13(1): 187. IF: 6.80.一区. Top.

    3. Association between modified frailty index and postoperative delirium in patients after cardiac surgery: a cohort study of 2080 older adults. CNS Neuroscience & Therapeutics 2024, Accepted. IF: 5.50. 一区

    4. ICU admission Braden score independently predicts delirium in critically ill patients with ischemic stroke. Intensive & Critical Care Nursing 2024, 82: 103626. IF: 5.30. 护理一区

    5. Braden score can independently predict mortality in critically ill patients with dementia. International Journal of Geriatric Psychiatry 2024, Accepted. IF: 4.00. 三区

    6. Effect of a Fall within Three Months of Admission on Delirium in Critically Ill Elderly Patients: A Population-based Cohort Study. Aging Clinical and Experimental Research 2024, Accepted. IF: 4.00. 三区

    7. Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis. BMC Infectious Diseases 2024, 24(1): 442. IF: 3.70. 三区

    8. Association between the ROX index and mortality in patients with acute hypoxemic respiratory failure: a retrospective cohort study. Respiratory Research 2024, 25(1): 143. IF: 5.80.

    9. Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis. BMC Medical Informatics and Decision Making 2024, 24(1): 84. IF: 3.50.

    10. Does ICU admission dysphagia independently contribute to delirium risk in ischemic stroke patients? Results from a cohort study. BMC Psychiatry 2024, 24(1): 65. IF: 4.40.

    11. Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study. Journal of Clinical Nursing 2024, 33(6): 2209-2225. IF: 4.20.

    12. Venous thromboembolism prophylaxis and mortality in patients with spinal fractures in ICUs. Nursing in Critical Care 2024, 29(3): 564-572. IF: 3.00.

    13. Factor Analysis Based on SHapley Additive exPlanations for Sepsis-Associated Encephalopathy in ICU Mortality Prediction Using XGBoost-A Retrospective Study Based on Two Large Database. Frontiers in Neurology 2023, 14: 1290117. IF: 3.40.

    14. Geriatric nutritional risk index independently predicts delirium in older patients in intensive care units: A multicenter cohort study. Archives of Gerontology and Geriatrics 2024, 118: 105288. IF: 4.00.

    15. Adverse Impact of Sodium Bicarbonate Administration on Multiple Outcomes in Acute Pancreatitis Patients With Hyperlactatemia. Pancreas 2024, 53(1): e62-e68. IF: 2.90. 四区

    16. Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study. BMC Geriatrics 2023, 23: 701. IF: 4.09.

    17. Associations Between Dysphagia and Adverse Health Outcomes in Older Adults with Dementia in Intensive Care Units: a Retrospective Cohort Study. Clinical Interventions in Aging 2023, 18: 1233-1248. IF: 3.60.

    18. A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease. Frontiers in Medicine 2023, 10: 1177786. IF: 3.90.

    19. Analysis of pathogenic factors on the death rate of sepsis patients. PLOS ONE 2023, 18(12): e0287254. IF: 3.70.

    20. Development and validation of a nomogram for predicting hospitalization longer than 14 days in pediatric patients with ventricular septal defect - a study based on the PIC database. Frontiers in Physiology 2023, 14: 1182719. IF: 4.00.

    21. Effect of early vasopressin combined with norepinephrine on short-term mortality in septic shock. American Journal of Emergency Medicine 2023, 69: 188-194. IF: 3.60.

    22. PaCO2 Levels at Admission Influence the Prognosis of Sepsis Patients: A Nonlinear Relationship. Journal of Translational Critical Care Medicine 2023, 5: e00012.

    23. Association Between Percutaneous Oxygen Saturation and Mortality of Patients with Mild Traumatic Brain Injury at ICU Admission: An Analysis of the MIMIC-III Database. Advances in Therapy 2023, 40(6): 2773-2783. IF: 3.80.

    24. The timing of initiating hydrocortisone and long-term mortality in septic shock. Anesthesia & Analgesia 2023, 137(4): 850-858. IF: 5.70.

    25. Norepinephrine combined with phenylephrine versus norepinephrine in patients with septic shock: a retrospective cohort study. BMC Infectious Diseases 2023, 23: 221. IF: 3.70.

    26. Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis. BMC Infectious Diseases 2023, 23: 90. IF: 3.70.

    27. Malignant cancer may increase the risk of all-cause in-hospital mortality in patients with acute myocardial infarction: a multicenter retrospective study of two large public databases. Cardio-Oncology 2023, 9(1): 6. IF: 3.30.

    28. Developing an Explainable Machine Learning Model to Predict the Mechanical Ventilation Duration of Patients with ARDS in Intensive Care Units. Heart & Lung 2023, 58: 74-81. IF: 2.80.

    29. Serum Anion Gap Level Predicts All-Cause Mortality in Septic Patients: A Retrospective Study Based on the MIMIC III Database. Journal of Intensive Care Medicine 2023, 38(4): 349-357. IF: 3.10.

    30. The Hemoglobin-to-Red Cell Distribution Width Ratio to Predict All-Cause Mortality in patients with Sepsis-Associated Encephalopathy in the MIMIC-IV Database. International Journal of Clinical Practice 2022, 2022: 7141216. IF: 2.60.

    31. Infections in Acute Pancreatitis: organisms, resistance-patterns and effect on mortality. Digestive Diseases and Sciences 2022, https://doi.org/10.1007/s10620-022-07793-1. IF: 3.10.

    32. Impact of falls within 3 months on the short-term prognoses of elderly patients in intensive care units: a retrospective cohort study using stabilized inverse probability treatment weighting. Clinical Interventions in Aging 2022, 17: 1779-1792. IF: 3.60.

    33. Association of lactate to albumin ratio and bicarbonate with short-term mortality risk in patients with acute myocardial infarction. BMC Cardiovascular Disorders 2022, 22: 490. IF: 2.10.

    34. Thiamine supplementation may be associated with improved prognosis in patients with sepsis: an analysis of the MIMIC-IV database. British Journal of Nutrition 2022, https://www.doi.org/10.1017/S0007114522003373. IF: 3.60.

    35. Development and validation of a simple nomogram for predicting the short-term prognosis of patients with pulmonary embolism. Heart & Lung 2023, 57: 144-151. IF: 2.8.

    36. Developing an Ensemble Machine Learning Model for Early Prediction of Sepsis-Associated Acute Kidney Injury. iScience 2022, 25(9): 104932. IF: 5.80.

    37. Effects of growth trajectory of shock index within 24 h on the prognosis of patients with sepsis. Frontiers in Medicine 2022, 9: 898424. IF: 3.90.

    38. Association between statin use and the prognosis of patients with acute myocardial infarction complicated with diabetes. Frontiers in Cardiovascular Medicine 2022, 9: 976656. IF: 3.60.

    39. The Relationship between Hematocrit and Serum Albumin Levels Difference and Mortality in Elderly Sepsis Patients in Intensive Care Units – A Retrospective Study Based on Two Large Database. BMC Infectious Diseases 2022, 22: 629. IF: 3.70.

    40. External Validation Based on Transfer Learning for Diagnosing ICUs Atelectasis Using Portable Chest X-rays. Frontiers in Medicine 2022, 9: 920040. IF: 3.90.

    41. Anti-embolism devices therapy to improve the ICU mortality rate of patients with acute myocardial infarction and type II diabetes mellitus. Frontiers in Cardiovascular Medicine 2022, 9: 948924. IF: 3.60.

    42. Metformin protects cardiovascular health in people with diabetes. Frontiers in Cardiovascular Medicine 2022, 9: 949113. IF: 3.60.

    43. Thiamine may be beneficial for patients with ventilator-associated pneumonia in the intensive care unit: a retrospective study based on the MIMIC-IV database. Frontiers in Pharmacology 2022, 13: 898566. IF: 5.60.

    44. The Use of Antibiotics for Ventilator-Associated Pneumonia in the MIMIC-IV Database. Frontiers in Pharmacology 2022, 13: 869499. IF: 5.60.

    45. Deep Transfer Learning to Quantify Pleural Effusion Severity in Chest X-rays. BMC Medical Imaging 2022, 22: 100. IF: 2.70.

    46. Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study. BMJ open 2022, 12: e059761. IF: 2.90.

    47. The association between bronchoscopy and the prognoses of patients with ventilator-associated pneumonia in intensive care units: a retrospective study based on the MIMIC-IV database. Frontiers in Pharmacology 2022, 13: 868920. IF: 5.60.

    48. Infusion of human albumin on acute pancreatitis therapy: new tricks for old dog? Frontiers in Pharmacology 2022, 13: 842108. IF: 5.60.

    49. Effects of Gastric Acid Secretion Inhibitors for Ventilator-Associated Pneumonia. Frontiers in Pharmacology 2022, 13: 898422. IF: 5.60.

    50. Using Restricted Cubic Splines to Study the duration of antibiotic use in the Prognosis of Ventilator-Associated Pneumonia. Frontiers in Pharmacology 2022, 13: 898630. IF: 5.60.

    51. Prognostic data analysis of surgical treatments for intracerebral hemorrhage. Neurosurgical Review 2022, 45(4):2733-2744. IF: 2.80.

    52. 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, 9: 879861. IF: 3.90.

    53. Antithrombotic therapy improves ICU mortality of septic patients with peripheral vascular disease. International Journal of Clinical Practice 2022, 2022: 1288535. IF: 2.80.

    54. Association Between Blood Pressure During Vasopressor Weaning and Hospital Survival: What are the Optimal Targets of Vasopressor Support? Emergencia 2022, 34(5): 331-338. IF: 5.50.

    55. 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. IF: 2.50.

    56. Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest). BMC Emergency Medicine 2022, 22: 16. IF: 2.50.

    57. 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. IF: 2.80.

    58. Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression. Frontiers in Cardiovascular Medicine 2022, 9: 847206. IF: 3.60.

    59. 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. IF: 3.60.

    60. 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, https://doi.org/10.1155/2022/4802702. IF: 3.464.

    61. 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.

    62. Risk factor analysis and Nomogram for predicting In-Hospital Mortality in ICU patients with sepsis and lung infection. BMC Pulmonary Medicine 2021, 22: 17. IF: 3.32.

    63. 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. IF: 8.44.

    64. Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units. Computational and Mathematical Methods in Medicine 2021, 2021:5745304. IF: 2.809.

    65. 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. IF: 3.149.

    66. Obesity paradox of all-cause mortality in 4133 patients treated with coronary revascularization. Journal of Interventional Cardiology 2021, 3867735. IF: 1.776.

    67. Influence of fluid balance on the prognosis of patients with sepsis. BMC Anesthesiology 2021, 21(1): 269. IF: 2.376.

    68. A new scoring system for predicting in-hospital death in patients having liver cirrhosis with esophageal varices. Frontiers in Medicine, 2021, 8: 678646. IF: 5.058.

    69. 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, 2021:9190908. IF: 1.621.

    70. 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. IF: 1.573.

    71. 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. IF: 5.846.

    72. 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. IF: 5.058.

    73. Prognostic Value of Blood Urea Nitrogen/Creatinine Ratio for Septic Shock: An Analysis of the MIMIC-III Clinical Database. BioMed Research International 2021, 2021: 5595042. IF: 3.246.

    74. Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection. Frontiers in Medicine 2021, 8: 671184. IF: 5.058.

    75. Effects of stress hyperglycemia on short-term prognosis of patients without diabetes mellitus in Coronary Care Unit. Frontiers in Cardiovascular Medicine 2021, 8: 683932. IF: 5.846.

    76. 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. IF: 1.573.

    77. Body Mass Index Linked to Short-Term and Long-Term All-Cause Mortality in Patients with Acute Myocardial Infarction. Postgraduate Medical Journal 2022, 98: e15. IF: 4.973.

    78. 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. IF: 1.287.

    79. Description of clinical characteristics of VAP patients in MIMIC database. Frontiers in Pharmacology 2019, 10: 62. IF: 4.225.

    80.  急性心肌梗死患者短期死亡风险预测模型的构建与评估. 中国循证心血管医学杂志, 2022, 14(4): 406-410.

    81. 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.

    82. 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.

    83. 胸腔X射线影像数据库-MIMIC-CXR数据探索. 中国循证心血管医学杂志, 2021,13(6): 653-656, 660.


    • SEER数据库论文：

    1. How to use the SEER (Surveillance, Epidemiology, and End Results) data: research design and methodology. Military Medical Research 2023, 10: 50. IF: 21.10.

    2. A prognostic nomogram for the cancer-specific survival rate of choroidal melanoma using the Surveillance, Epidemiology, and End Results database. Frontiers in Medicine 2024, Accepted. IF: 3.90. 三区

    3. Joinpoint Regression Analysis of Recent Trends In Desmoplastic Malignant Melanoma Incidence And Mortality: 15-Year Multicentre Retrospective Study. Archives of Dermatological Research, 2024, Accepted. IF: 2.99.

    4. Competing risk nomogram predicting cause-specific mortality in older patients with testicular germ cell tumors. Frontiers in Medicine 2024, 11: 1327485. IF: 3.90.

    5. Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study. Frontiers in Endocrinology, 2024, 15: 1260966. IF: 5.20.

    6. Competing risks model for predicting the prognosis of patients with angiosarcoma based on the SEER database of 3905 cases. Holistic Integrative Oncology 2024, 3(1): 13.

    7. New findings in prognostic factor assessment for adenocarcinoma of transverse colon: a comparison study between competing-risk and COX regression analysis. Frontiers in Medicine 2024, 11: 1301487. IF: 3.90.

    8. Competing-Risks Analysis for Evaluating the Prognosis of Patients with Microinvasive Cutaneous Squamous Cell Carcinoma Based on the SEER Database. BMC Medical Research Methodology 2023, 23: 286. IF: 4.00.

    9. Deep-Learning-Based Survival Prediction of Patients with lower limb melanoma. Discover Oncology 2023, 14: 218. IF: 2.20.

    10. Analysis and Prediction of 5-Year Survival in Patients with Cutaneous Melanoma: a Model-Based Period Analysis. Frontiers in Endocrinology 2023, 14: 1238086. IF: 5.20.

    11. A comprehensive prognostic analysis of cause-specific mortality in patients with ovarian serous cystadenocarcinoma using a competing-risks model: a case study of the SEER database. European Review for Medical and Pharmacological Sciences 2023, 27: 11143-11155. IF: 3.30.

    12. Establishment of a prognostic nomogram for cancer-specific survival in patients with base-of-tongue squamous cell carcinoma: a retrospective study based on the SEER database. Cancer Control 2023, 30: 10732748231210733. IF: 2.60.

    13. A prognostic nomogram for the cancer-specific survival of white patients with invasive melanoma at BANS sites based on the Surveillance, Epidemiology, and End Results database. Frontiers in Medicine 2023, 10: 1177786. IF: 3.90.

    14. Development and Validation of a Nomogram for Predicting the Overall Survival of Patients with Testicular Cancer. Cancer Medicine 2023, 12: 15567–15578. IF: 4.00.

    15. Prediction of death probability in adenocarcinoma of the transverse colon: competing-risk nomograms based on 21,469 patients. Journal of Cancer Research and Clinical Oncology 2023, 149(12):10435-10452. IF: 3.60.

    16. Deep-Learning-Based Survival Prediction of Patients with Cutaneous Malignant Melanoma. Frontiers in Medicine 2023, 10: 1165865. IF: 3.90.

    17. Causes of Death in Endometrial Cancer Survivors: A Surveillance, Epidemiology, and End Results Based Analysis. Cancer Medicine 2023, 12(9): 10917-10930. IF: 4.00.

    18. Crafting a prognostic nomogram for the overall survival rate of cutaneous verrucous carcinoma using the surveillance, epidemiology, and end results database. Frontiers in Endocrinology 2023, 14: 1142014. IF: 5.20.

    19. Prognosis of the keratinizing squamous cell carcinoma of the tongue based on Surveillance, Epidemiology, and End Results database. International Journal of Clinical Practice 2023, 2023: 3016994. IF: 2.80.

    20. Nomograms for predicting overall survival and cancer-specific survival in patients with head and neck non-Hodgkin lymphoma: a population-based study. Medicine 2023, 102(6): e32865. IF: 1.60.

    21. A nomogram for predicting survival in patients with skin nonkeratinizing squamous cell carcinoma: a study based on the Surveillance, Epidemiology, and End Results database. Frontiers in Medicine 2023, 10: 1082402. IF: 3.90.

    22. Analysis of Prognostic Factors of Low-Grade Gliomas in Adults Using Time-Dependent Competing Risk Models: A Population Study Based on the SEER Database. Cancer Control 2022, 29: 10732748221143388. IF: 2.60.

    23. Recent estimates and predictions of 5-year survival in patients with pancreatic cancer: a model-based period analysis. Frontiers in Medicine 2022, 9: 1049136. IF: 3.90.

    24. Recent trends in synchronous brain metastases incidence and mortality in the United States: ten-year multicenter experience. Future oncology 2022, 29: 8374–8389. IF: 3.30.

    25. The incidence trend of papillary thyroid carcinoma in the United States during 2003–2017. Cancer Control 2022, 29: 10732748221135447. IF: 2.60.

    26. Factors associated with lymph node yield and effects of lymph node density on survival of patients with pulmonary sarcomatoid carcinoma. American Journal of Clinical Oncology 2022, 45(11): 458-464. IF: 2.60.

    27. Analysis and prediction of relative survival trends in patients with non-Hodgkin lymphoma in the United States using a model-based period analysis method. Frontiers in Oncology 2022, 12: 942122. IF: 4.70.

    28. Machine learning models for predicting survival in patients with ampullary adenocarcinoma. Asia-Pacific Journal of Oncology Nursing 2022, 9: 100141. IF: 1.80.

    29. Analysis of Suicide Risk in Adult US Patients with Squamous Cell Carcinoma: a Retrospective Study Based on the Surveillance, Epidemiology, and End Results Database. BMJ open 2022, 12: e061913. IF: 2.90.

    30. Association between age and the presence and mortality of breast cancer synchronous brain metastases in the United States: a neglected SEER analysis. Frontiers in Public Health 2022, 10: 1000415. IF: 5.20.

    31. Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: a Model-Based Period Analysis, 2001-2015. Cancer Control 2022, 29: 10732748221121226. IF: 2.60.

    32. Incidence and risk factors of suicide among patients with pancreatic cancer: a population-based analysis from 2000 to 2018. Frontiers in Oncology 2022, 12: 972908. IF: 4.70.

    33. Competing risk analysis of cardiovascular death in patients with primary gallbladder cancer. Cancer Medicine 2023, 12(3): 2179-2186. IF: 4.00.

    34. A novel nomogram for predicting cancer specific survival in women with uterine sarcoma: a large population based study. BMC Women’s Health 2022, 22: 175. IF: 2.50.

    35. Recent estimates and predictions of 5-year survival in patients with gastric cancer: a model-based period analysis. Cancer Control 2022, 29: 10732748221099227. IF: 2.60.

    36. Epidemiology, management, and long-term survival outcomes of intracranial typical site germinomas: an analysis of the surveillance, epidemiology, and end-results (SEER) database. Cancer Control 2022, 29: 10732748221095944. IF: 2.60.

    37. 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. IF: 3.80.

    38. 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, 82(4):415-424. IF: 4.012.

    39. Midlife Brain Metastases in the United States: Is Male at Risk? Cancer Medicine, 2022, 11(4):1202-1216. IF: 4.00.

    40. Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population-based Study. International Journal of General Medicine 2021, 14: 9841-9851. IF: 2.145.

    41. 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: 10732748211064034. IF: 2.339.

    42. 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. IF: 2.145.

    43. Socioeconomic status and adult gliomas mortality risk: An observational study based on SEER data. World Neurosurgery 2021, 155: e131-e141. IF: 2.21.

    44. Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: a SEER-based study. Cancer Control 2021, 28: 10732748211036775. IF: 2.339.

    45. Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis. BMJ open 2021, 11: e048243. IF: 3.006.

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

    47. 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. IF: 2.876.

    48. Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: a population-based analysis. Cancer Medicine 2021, 10(11): 3756-3769. IF: 4.711. A2-3区

    49. Competing-risks nomograms for predicting the prognosis of patients with infiltrating lobular carcinoma of the breast. Clinical Breast Cancer 2021, 21(6):e704-e714. IF: 3.078.

    50. 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. IF: 2.145.

    51. Prognostic exploration of all-cause death in gingival squamous cell carcinoma: a retrospective analysis of 2076 patients. Journal of Oncology 2021, 2021:6676587. IF: 4.501.

    52. Competitive risk analysis of prognosis in patients with cecum cancer: a populationbased study. Cancer Control 2021, 28: 1073274821989316. IF: 2.339.

    53. 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. IF: 3.616.

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

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

    56. Prognostic factors in patients with rhabdomyosarcoma using competing-risks analysis: a study of cases in the SEER database. Journal of Oncology 2020, 2020:2635486. IF: 4.375.

    57. 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. IF: 3.302.

    58. 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. IF: 1.889.

    59. 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. IF: 2.649.

    60. 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. IF: 4.43.

    61. 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. IF: 1.241.

    62. Incidence of and sociological risk factors for suicide death in patients with leukemia: a population-based study. Journal of International Medical Research 2020, 48(5):300060520922463. IF: 1.671.

    63. 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. IF: 2.571.

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

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

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

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

    68. 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. IF: 3.264

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

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

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

    72. 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. IF: 2.13.

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

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

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

    76. 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. IF: 2.879.

    77. 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. IF: 3.491.

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

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

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

    81. 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. IF: 3.959.

    82. 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. IF: 2.886.

    83. 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. IF: 2.381

    84. 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. IF: 2.243

    85. 基于SEER数据库的胰腺癌患者自杀风险因素分析. 医学新知, 2022, 32(03): 192-200.

    86. 乳腺髓样癌临床预测模型的建立和验证：基于SEER数据库. 医学新知, 2023, 33(03): 163-172.

    87. 社会经济学地位与盲肠腺癌死亡风险的关系：一项基于美国人群的分析. 南方医科大学学报, 2023, 43(8): 1417-1424.


    • 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. IF: 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. IF: 11.059

    3. Global, regional, and national childhood cancer burden, 1990 - 2019: an analysis based on the Global Burden of Disease Study 2019. Journal of Advanced Research 2022, 40: 233-247. IF: 10.70.

    4. 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. IF: 10.392

    5. Global Burden of Thyroid Cancer From 1990-2017. JAMA Network Open 2020, 3(6): e208759. IF: 8.483

    6. Secular trends in the incidence of eating disorders in China from 1990 to 2017: a joinpoint and age–period–cohort analysis. Psychological Medicine 2022, 52(5): 946-956. IF: 6.90.

    7. 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. IF: 6.892

    8. 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. IF: 6.892

    9. Dietary risk related colorectal cancer burden: Estimates from 1990 to 2019. Frontiers in Nutrition 2021, 8: 690663. IF: 6.59.

    10. Health effects of tobacco at the global, regional, and national levels: results from the 2019 Global Burden of Disease study. Nicotine & Tobacco Research 2021, 24(6): 864-870. IF: 5.825.

    11. 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. IF: 5.682

    12. The Burden of Early-Onset Colorectal Cancer and Its Risk Factors from 1990 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Cancers 2022, 14: 3502. IF: 5.20.

    13. 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. IF: 4.791.

    14. 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. IF: 4.791.

    15. The burden and trend of gastric cancer and possible risk factors in five Asian countries from 1990 to 2019. Scientific Reports 2022, 12: 5980. IF: 4.60.

    16. 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. IF: 4.232

    17. 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. IF: 4.003.

    18. 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. IF: 3.918

    19. 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. IF: 3.90.

    20. Worldwide long-term trends in the incidence of nonalcoholic fatty liver disease during 1990–2019: a joinpoint and age-period-cohort analysis. Frontiers in Cardiovascular Medicine 2022, 9: 891963. IF: 3.60. 二区

    21. 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. IF: 3.295.

    22. 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. IF: 3.295.

    23. 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. IF: 3.147.

    24. 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.

    25. 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.


    • UK Biobank数据库论文：

    1. Associations of HDL-C/LDL-C with myocardial infarction, all-cause mortality, hemorrhagic stroke and ischemic stroke: a longitudinal study based on 384,093 participants from the UK Biobank. Stroke & Vascular Neurology 2023, 8(2): e001668. IF: 5.90. 一区top

    2. Associations of unhealthy lifestyle and nonalcoholic fatty liver disease with cardiovascular healthy outcomes. Journal of the American Heart Association 2023, 12: e031440. IF: 5.40. 一区top

    3. Body mass index, genetic susceptibility, and Alzheimer's disease: A longitudinal study based on 475,813 participants from the UK Biobank. Journal of Translational Medicine 2022, 20: 417. IF: 7.40. 二区

    4. Feelings of tense and risk of incident dementia: a prospective study of 482,360 individuals. Journal of Affective Disorders 2024, https://10.1016/j.jad.2024.01.156. IF: 6.60. 二区top

    5. Dietary Inflammatory Index, Genetic Susceptibility and Risk of Incident Dementia: A Prospective Cohort Study from UK Biobank. Journal of Neurology 2023, https://doi.org/10.1007/s00415-023-12065-7. IF: 6.00. 二区

    6. The association of night shift work with the risk of all-cause dementia and Alzheimer's disease: a longitudinal study of 245,570 UK Biobank participants. Journal of Neurology 2023, 270(7): 3499-3510. IF: 6.00. 二区

    7. Association of birthweight with risk of incident dementia: a prospective cohort study. GeroScience 2024, https://doi.org/10.1007/s11357-024-01105-3. IF: 5.60. 二区

    8. Associations of folate/folic acid supplementation alone and in combination with other B vitamins on dementia risk and brain structure: evidence from 466,224 UK Biobank participants. Journals of Gerontology Series A: Biological Sciences and Medical Sciences 2024, 79(4): glad266. IF: 5.10. 二区top

    9. Associations between the use of common nonsteroidal anti-inflammatory drugs, genetic susceptibility and dementia in participants with chronic pain: a prospective study based on 194,758 participants from the UK Biobank. Journal of Psychiatric Research 2023, 169: 152-159. IF: 4.80. A1二区

    10. Associations of screen-based sedentary activities with all cause dementia, Alzheimer’s disease, vascular dementia: a longitudinal study based on 462,524 participants from the UK Biobank. BMC Public Health 2023, 23: 2141. IF: 4.50. 二区

    11. Associations of air pollution with all-cause dementia, Alzheimer's disease, and vascular dementia: a prospective cohort study based on 437,932 participants from the UK Biobank. Frontiers in Neuroscience 2023, 17: 1216686. IF: 4.30. 二区

    12. Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study. BMC Geriatrics 2022, 22: 638. IF: 4.09. 二区

    13. Evaluating the effect of kidney function on brain volumes and dementia risk in the UK Biobank. Archives of Gerontology and Geriatrics 2024, 116: 105157. IF: 4.00. 二区

    14. Association of NAFLD with cardiovascular disease and all-cause mortality: A large scale prospective cohort study based on UK Biobank. Therapeutic Advances in Chronic Disease 2022, 13: 1-19. IF: 3.50.

    15. 基于文献计量学的痴呆在英国生物银行数据库的研究现状. 中国循证医学杂志, 2023, 23(4): 426-432.

    16. UK Biobank 数据的应用探索. 中国循证医学杂志, 2022, 22(9): 1099-1107.

    17. 糖尿病,遗传易感性和阿尔茨海默病：基于英国生物数据库的回顾性队列研究. 老年医学与保健, 2022, 28(2): 231-235+241.

    18. 如何利用UK Biobank申请研究数据和生物样本. 中国循证心血管医学杂志, 2018, 10(12): 1450-1453.



    • NHANES数据库论文：

    1. Association between second-hand smoke exposure and serum sex hormone concentrations among US female adults: a cross-sectional analysis using data from the National Health and Nutrition Examination Survey, 2013–2016. BMJ open 2024, Accepted. IF: 2.90.

    2. The role of hypertension in bone mineral density among males older than 50 years and postmenopausal females: evidence from the US National Health and Nutrition Examination Survey, 2005-2010. Frontiers in Public Health 2023, 11: 1142155. IF: 5.20.

    3. Association between periodontitis and osteoporosis in United States adults from the National Health and Nutrition Examination Survey: a cross-sectional analysis. BMC Oral Health 2023, 23: 254. IF: 2.90.

    4. Machine learning for the prediction of cognitive impairment in older adults. Frontiers in Neuroscience 2023, 17: 1158141. IF: 4.30.

    5. Nonlinear relationship between dietary vitamin E intake and cognitive performance in older adults. Public Health 2023, 219: 10-17. IF: 5.20.

    6. Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans. Frontiers in Neuroscience 2023, 17: 1117056. IF: 4.30.

    7. Metformin protects cardiovascular health in people with diabetes. Frontiers in Cardiovascular Medicine 2022, 9: 949113. IF: 3.60.

    8. Relationship between self-reported sleep duration during week-/work-days and metabolic syndrome from NHANES 2013–2016. Sleep and Breathing 2022, 26(4): 1593-1601. IF: 2.50.

    9. 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. IF: 2.145.

    10. 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. IF: 3.367.

    11. 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. IF: 3.655.

    12. Physical Activity Levels and Diabetes Prevalence in US Adults: Findings from NHANES 2015-2016. Diabetes Therapy 2020, 11(6): 1303-1316. IF:2.945.


    • 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.


    • 临床大数据挖掘类SCI综述：

    1. How to use the SEER (Surveillance, Epidemiology, and End Results) data: research design and methodology. Military Medical Research 2023, 10: 50. IF: 21.10.

    2. Exploring patient medication adherence and data mining methods in clinical big data: a contemporary review. Journal of Evidence-Based Medicine 2023, 16(3): 342-375. IF: 7.30.

    3. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Military Medical Research 2021, 8: 44. IF: 34.915. ESI热点论文

    4. Brief introduction of medical database and data mining technology in big data era. Journal of Evidence-Based Medicine 2020, 13: 57-69. IF: 6.224. ESI高被引论文


    • 指导本科生发表论文：

    1. Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: a Model-Based Period Analysis, 2001-2015. Cancer Control 2022, 29: 10732748221121226. IF: 2.60

    2. 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. IF: 2.145.

    3. 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. IF: 2.13.

    4. 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. IF: 3.264

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

    6. 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.

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


    七、重要社会任职（兼职）

    • 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

    • 中国医疗保健国际交流促进会循证医学分会，常务委员

    • 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

    • 广东省计算机学会大数据专业委员会，委员