BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

250 related articles for article (PubMed ID: 36579020)

  • 1. Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment.
    Bai Y; Xia J; Huang X; Chen S; Zhan Q
    Front Physiol; 2022; 13():1050849. PubMed ID: 36579020
    [No Abstract]   [Full Text] [Related]  

  • 2. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.
    Huang B; Liang D; Zou R; Yu X; Dan G; Huang H; Liu H; Liu Y
    Ann Transl Med; 2021 May; 9(9):794. PubMed ID: 34268407
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.
    Jiang Z; Liu L; Du L; Lv S; Liang F; Luo Y; Wang C; Shen Q
    Heliyon; 2024 Mar; 10(6):e28143. PubMed ID: 38533071
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment.
    Liu X; Jiang Y; Jia X; Ma X; Han C; Guo N; Peng Y; Liu H; Ju Y; Luo X; Li X; Bu Y; Zhang J; Liu Y; Gao Y; Zhao M; Wang H; Luo L; Yu K; Wang C
    Crit Care; 2021 Aug; 25(1):320. PubMed ID: 34461969
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis.
    Maddali MV; Churpek M; Pham T; Rezoagli E; Zhuo H; Zhao W; He J; Delucchi KL; Wang C; Wickersham N; McNeil JB; Jauregui A; Ke S; Vessel K; Gomez A; Hendrickson CM; Kangelaris KN; Sarma A; Leligdowicz A; Liu KD; Matthay MA; Ware LB; Laffey JG; Bellani G; Calfee CS; Sinha P;
    Lancet Respir Med; 2022 Apr; 10(4):367-377. PubMed ID: 35026177
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
    Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
    Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
    [TBL] [Abstract][Full Text] [Related]  

  • 7. [Predictive value of machine learning for in-hospital mortality for trauma-induced acute respiratory distress syndrome patients: an analysis using the data from MIMIC III].
    Tang R; Tang W; Wang D
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Mar; 34(3):260-264. PubMed ID: 35574742
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Diagnostic value of mechanical power in patients with moderate to severe acute respiratory distress syndrome: an analysis using the data from MIMIC-III].
    Yan Y; Xie Y; Wang Y; Chen X; Sun Y; Du Z; Li X
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Jan; 34(1):35-40. PubMed ID: 35307058
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.
    Sinha P; Churpek MM; Calfee CS
    Am J Respir Crit Care Med; 2020 Oct; 202(7):996-1004. PubMed ID: 32551817
    [No Abstract]   [Full Text] [Related]  

  • 10. MACHINE LEARNING MODELS FOR PREDICTING ACUTE KIDNEY INJURY IN PATIENTS WITH SEPSIS-ASSOCIATED ACUTE RESPIRATORY DISTRESS SYNDROME.
    Zhou Y; Feng J; Mei S; Zhong H; Tang R; Xing S; Gao Y; Xu Q; He Z
    Shock; 2023 Mar; 59(3):352-359. PubMed ID: 36625493
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.
    Lin S; Yang M; Liu C; Wang Z; Long X
    Int J Med Inform; 2024 Jun; 186():105397. PubMed ID: 38507979
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms.
    Wang R; Cai L; Zhang J; He M; Xu J
    Medicina (Kaunas); 2023 Jan; 59(1):. PubMed ID: 36676795
    [No Abstract]   [Full Text] [Related]  

  • 13. A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study.
    Xu C; Zheng L; Jiang Y; Jin L
    BMC Pulm Med; 2023 Mar; 23(1):78. PubMed ID: 36890503
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The Association Between Etiologies and Mortality in Acute Respiratory Distress Syndrome: A Multicenter Observational Cohort Study.
    Wang Y; Zhang L; Xi X; Zhou JX;
    Front Med (Lausanne); 2021; 8():739596. PubMed ID: 34733862
    [No Abstract]   [Full Text] [Related]  

  • 15. A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning.
    Yang B; Zhu Y; Lu X; Shen C
    Front Endocrinol (Lausanne); 2022; 13():917838. PubMed ID: 35846312
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome.
    Wei S; Zhang Y; Dong H; Chen Y; Wang X; Zhu X; Zhang G; Guo S
    BMC Pulm Med; 2023 Oct; 23(1):370. PubMed ID: 37789305
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Machine learning-derived blood culture classification with both predictive and prognostic values in the intensive care unit: A retrospective cohort study.
    Zhang J; Liu W; Xiao W; Liu Y; Hua T; Yang M
    Intensive Crit Care Nurs; 2024 Feb; 80():103549. PubMed ID: 37804818
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study.
    Li H; Gu Y; Liu X; Yi X; Li Z; Yu Y; Yu T; Li L
    Healthcare (Basel); 2022 Oct; 10(11):. PubMed ID: 36360490
    [No Abstract]   [Full Text] [Related]  

  • 19. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter.
    Guo F; Zhu X; Wu Z; Zhu L; Wu J; Zhang F
    J Transl Med; 2022 Jun; 20(1):265. PubMed ID: 35690822
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012.
    Dellinger RP; Levy MM; Rhodes A; Annane D; Gerlach H; Opal SM; Sevransky JE; Sprung CL; Douglas IS; Jaeschke R; Osborn TM; Nunnally ME; Townsend SR; Reinhart K; Kleinpell RM; Angus DC; Deutschman CS; Machado FR; Rubenfeld GD; Webb SA; Beale RJ; Vincent JL; Moreno R;
    Crit Care Med; 2013 Feb; 41(2):580-637. PubMed ID: 23353941
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.