BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1898 related articles for article (PubMed ID: 33035175)

  • 1. Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
    Pan P; Li Y; Xiao Y; Han B; Su L; Su M; Li Y; Zhang S; Jiang D; Chen X; Zhou F; Ma L; Bao P; Xie L
    J Med Internet Res; 2020 Nov; 22(11):e23128. PubMed ID: 33035175
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
    Zhou S; Lu Z; Liu Y; Wang M; Zhou W; Cui X; Zhang J; Xiao W; Hua T; Zhu H; Yang M
    Eur J Med Res; 2024 Jan; 29(1):14. PubMed ID: 38172962
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
    Huang J; Jin W; Duan X; Liu X; Shu T; Fu L; Deng J; Chen H; Liu G; Jiang Y; Liu Z
    Front Public Health; 2022; 10():1086339. PubMed ID: 36711330
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
    Li J; Liu S; Hu Y; Zhu L; Mao Y; Liu J
    J Med Internet Res; 2022 Aug; 24(8):e38082. PubMed ID: 35943767
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning-Based Development and Validation Study.
    Wang H; Wu W; Han C; Zheng J; Cai X; Chang S; Shi J; Xu N; Ai Z
    JMIR Med Inform; 2021 Nov; 9(11):e30079. PubMed ID: 34806984
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.
    Chen Z; Li T; Guo S; Zeng D; Wang K
    Front Cardiovasc Med; 2023; 10():1119699. PubMed ID: 37077747
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.
    Ye Z; An S; Gao Y; Xie E; Zhao X; Guo Z; Li Y; Shen N; Ren J; Zheng J
    Eur J Med Res; 2023 Jan; 28(1):33. PubMed ID: 36653875
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
    Chen X; Pan J; Li Y; Tang R
    Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.
    Liu X; Hu P; Yeung W; Zhang Z; Ho V; Liu C; Dumontier C; Thoral PJ; Mao Z; Cao D; Mark RG; Zhang Z; Feng M; Li D; Celi LA
    Lancet Digit Health; 2023 Oct; 5(10):e657-e667. PubMed ID: 37599147
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.
    Yu R; Wang S; Xu J; Wang Q; He X; Li J; Shang X; Chen H; Liu Y
    Brain Inj; 2021 Dec; 35(14):1658-1664. PubMed ID: 35080996
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations.
    Zou Y; Shi Y; Sun F; Liu J; Guo Y; Zhang H; Lu X; Gong Y; Xia S
    Comput Methods Programs Biomed; 2022 Oct; 225():107038. PubMed ID: 35930861
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study.
    He F; Page JH; Weinberg KR; Mishra A
    J Med Internet Res; 2022 Jan; 24(1):e31549. PubMed ID: 34951865
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.
    Wang YX; Li XL; Zhang LH; Li HN; Liu XM; Song W; Pang XF
    Front Nutr; 2023; 10():1060398. PubMed ID: 37125050
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients.
    Wang K; Yan LZ; Li WZ; Jiang C; Wang NN; Zheng Q; Dong NG; Shi JW
    Front Cardiovasc Med; 2022; 9():863642. PubMed ID: 35800164
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.
    Fan Z; Jiang J; Xiao C; Chen Y; Xia Q; Wang J; Fang M; Wu Z; Chen F
    J Transl Med; 2023 Jun; 21(1):406. PubMed ID: 37349774
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
    Xiong W; Zhang L; She K; Xu G; Bai S; Liu X
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov; 34(11):1188-1193. PubMed ID: 36567564
    [TBL] [Abstract][Full Text] [Related]  

  • 17. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
    Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.
    Gao T; Nong Z; Luo Y; Mo M; Chen Z; Yang Z; Pan L
    Ren Fail; 2024 Dec; 46(1):2316267. PubMed ID: 38369749
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.
    Thorsen-Meyer HC; Nielsen AB; Nielsen AP; Kaas-Hansen BS; Toft P; Schierbeck J; Strøm T; Chmura PJ; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Belling K; Brunak S; Perner A
    Lancet Digit Health; 2020 Apr; 2(4):e179-e191. PubMed ID: 33328078
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.
    Abraham VM; Booth G; Geiger P; Balazs GC; Goldman A
    Clin Orthop Relat Res; 2022 Nov; 480(11):2137-2145. PubMed ID: 35767804
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 95.