These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

211 related articles for article (PubMed ID: 19061509)

  • 1. Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies.
    Verplancke T; Van Looy S; Benoit D; Vansteelandt S; Depuydt P; De Turck F; Decruyenaere J
    BMC Med Inform Decis Mak; 2008 Dec; 8():56. PubMed ID: 19061509
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Admission factors associated with hospital mortality in patients with haematological malignancy admitted to UK adult, general critical care units: a secondary analysis of the ICNARC Case Mix Programme Database.
    Hampshire PA; Welch CA; McCrossan LA; Francis K; Harrison DA
    Crit Care; 2009; 13(4):R137. PubMed ID: 19706163
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].
    Lin K; Xie JQ; Hu YH; Kong GL
    Beijing Da Xue Xue Bao Yi Xue Ban; 2018 Apr; 50(2):239-244. PubMed ID: 29643521
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h.
    Timsit JF; Fosse JP; Troché G; De Lassence A; Alberti C; Garrouste-Orgeas M; Azoulay E; Chevret S; Moine P; Cohen Y
    Intensive Care Med; 2001 Jun; 27(6):1012-21. PubMed ID: 11497133
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Acute Physiology and Chronic Health Evaluation II score for the assessment of mortality prediction in the intensive care unit: a single-centre study from Iran.
    Bahtouee M; Eghbali SS; Maleki N; Rastgou V; Motamed N
    Nurs Crit Care; 2019 Nov; 24(6):375-380. PubMed ID: 30924584
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting hospital mortality in critically ill cancer patients according to acute kidney injury severity.
    Libório AB; Abreu KL; Silva GB; Lima RS; Barreto AG; Barbosa OA; Daher EF
    Oncology; 2011; 80(3-4):160-6. PubMed ID: 21677465
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.
    Zimmerman JE; Kramer AA; McNair DS; Malila FM
    Crit Care Med; 2006 May; 34(5):1297-310. PubMed ID: 16540951
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Monocyte/lymphocyte ratio as a predictor of 30-day mortality and adverse events in critically ill patients: analysis of the MIMIC-III database].
    Li Y; Liu Y; Zhou C; Zhang Z; Zuo X; Li J; Cao Q
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 May; 33(5):582-586. PubMed ID: 34112297
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Performance of three prognostic models in critically ill patients with cancer: a prospective study.
    Martos-Benítez FD; Larrondo-Muguercia H; León-Pérez D; Rivero-López JC; Orama-Requejo V; Martínez-Alfonso JL
    Int J Clin Oncol; 2020 Jul; 25(7):1242-1249. PubMed ID: 32212014
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Performance of three prognostic models in patients with cancer in need of intensive care in a medical center in China.
    Xing X; Gao Y; Wang H; Huang C; Qu S; Zhang H; Wang H; Sun K
    PLoS One; 2015; 10(6):e0131329. PubMed ID: 26110534
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Hypomagnesaemia in critically ill patients with haematological malignancies.
    Namendys-Silva SA; Correa-García P; García-Guillén FJ; Texcocano-Becerra J; Colorado-Castillo G; Meneses-García A; Herrera-Gómez A
    Nutr Hosp; 2014 Jul; 30(1):183-7. PubMed ID: 25137279
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Simplified prognostic model for critically ill patients in resource limited settings in South Asia.
    Haniffa R; Mukaka M; Munasinghe SB; De Silva AP; Jayasinghe KSA; Beane A; de Keizer N; Dondorp AM
    Crit Care; 2017 Oct; 21(1):250. PubMed ID: 29041985
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Outcome in critically ill medical patients treated with renal replacement therapy for acute renal failure: comparison between patients with and those without haematological malignancies.
    Benoit DD; Hoste EA; Depuydt PO; Offner FC; Lameire NH; Vandewoude KH; Dhondt AW; Noens LA; Decruyenaere JM
    Nephrol Dial Transplant; 2005 Mar; 20(3):552-8. PubMed ID: 15671075
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The effectiveness of the APACHE II, SAPS II and SOFA prognostic scoring systems in patients with haematological malignancies in the intensive care unit.
    Sawicka W; Owczuk R; Wujtewicz MA; Wujtewicz M
    Anaesthesiol Intensive Ther; 2014; 46(3):166-70. PubMed ID: 25078769
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prognostic factors in critically ill patients with hematologic malignancies admitted to the intensive care unit.
    Yeo CD; Kim JW; Kim SC; Kim YK; Kim KH; Kim HJ; Lee S; Rhee CK
    J Crit Care; 2012 Dec; 27(6):739.e1-6. PubMed ID: 23217573
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model.
    Lin K; Hu Y; Kong G
    Int J Med Inform; 2019 May; 125():55-61. PubMed ID: 30914181
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records.
    Nielsen AB; Thorsen-Meyer HC; Belling K; Nielsen AP; Thomas CE; Chmura PJ; Lademann M; Moseley PL; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Perner A; Brunak S
    Lancet Digit Health; 2019 Jun; 1(2):e78-e89. PubMed ID: 33323232
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mortality prediction after cardiac surgery: blood lactate is indispensible.
    Badreldin AM; Doerr F; Elsobky S; Brehm BR; Abul-dahab M; Lehmann T; Bayer O; Wahlers T; Hekmat K
    Thorac Cardiovasc Surg; 2013 Dec; 61(8):708-17. PubMed ID: 24338631
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Performance evaluation of APACHE II score for an Indian patient with respiratory problems.
    Gupta R; Arora VK
    Indian J Med Res; 2004 Jun; 119(6):273-82. PubMed ID: 15243165
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
    Kong G; Lin K; Hu Y
    BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.