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 *

148 related articles for article (PubMed ID: 35836294)

  • 1. External validation of a machine learning model to predict hemodynamic instability in intensive care unit.
    Dung-Hung C; Cong T; Zeyu J; Yu-Shan OY; Yung-Yan L
    Crit Care; 2022 Jul; 26(1):215. PubMed ID: 35836294
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Early prediction of hemodynamic interventions in the intensive care unit using machine learning.
    Rahman A; Chang Y; Dong J; Conroy B; Natarajan A; Kinoshita T; Vicario F; Frassica J; Xu-Wilson M
    Crit Care; 2021 Nov; 25(1):388. PubMed ID: 34775971
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit.
    Potes C; Conroy B; Xu-Wilson M; Newth C; Inwald D; Frassica J
    Crit Care; 2017 Nov; 21(1):282. PubMed ID: 29151364
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. [Prediction of intensive care unit readmission for critically ill patients based on ensemble learning].
    Lin Y; Wu JY; Lin K; Hu YH; Kong GL
    Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Jun; 53(3):566-572. PubMed ID: 34145862
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study.
    Hur S; Min JY; Yoo J; Kim K; Chung CR; Dykes PC; Cha WC
    J Med Internet Res; 2021 Aug; 23(8):e23508. PubMed ID: 34382940
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study.
    Pirracchio R; Petersen ML; Carone M; Rigon MR; Chevret S; van der Laan MJ
    Lancet Respir Med; 2015 Jan; 3(1):42-52. PubMed ID: 25466337
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Predicting prolonged length of intensive care unit stay
    Wu JY; Lin Y; Lin K; Hu YH; Kong GL
    Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Dec; 53(6):1163-1170. PubMed ID: 34916699
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.
    Awad A; Bader-El-Den M; McNicholas J; Briggs J
    Int J Med Inform; 2017 Dec; 108():185-195. PubMed ID: 29132626
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting ICU hemodynamic instability using continuous multiparameter trends.
    Cao H; Eshelman L; Chbat N; Nielsen L; Gross B; Saeed M
    Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():3803-6. PubMed ID: 19163540
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MACHINE LEARNING FOR PREDICTING HEMODYNAMIC DETERIORATION OF PATIENTS WITH INTERMEDIATE-RISK PULMONARY EMBOLISM IN INTENSIVE CARE UNIT.
    Xu J; Hu Z; Miao J; Cao L; Tian Z; Yao C; Huang K
    Shock; 2024 Jan; 61(1):68-75. PubMed ID: 38010031
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.
    Mao Q; Jay M; Hoffman JL; Calvert J; Barton C; Shimabukuro D; Shieh L; Chettipally U; Fletcher G; Kerem Y; Zhou Y; Das R
    BMJ Open; 2018 Jan; 8(1):e017833. PubMed ID: 29374661
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.
    Gong KD; Lu R; Bergamaschi TS; Sanyal A; Guo J; Kim HB; Nguyen HT; Greenstein JL; Winslow RL; Stevens RD
    Anesthesiology; 2023 Mar; 138(3):299-311. PubMed ID: 36538354
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit.
    Yang J; Lim HG; Park W; Kim D; Yoon JS; Lee SM; Kim K
    J Crit Care; 2022 Oct; 71():154106. PubMed ID: 35834893
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.
    Desautels T; Das R; Calvert J; Trivedi M; Summers C; Wales DJ; Ercole A
    BMJ Open; 2017 Sep; 7(9):e017199. PubMed ID: 28918412
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.
    Govindan S; Spicer A; Bearce M; Schaefer RS; Uhl A; Alterovitz G; Kim MJ; Carey KA; Shah NS; Winslow C; Gilbert E; Stey A; Weiss AM; Amin D; Karway G; Martin J; Edelson DP; Churpek MM
    Crit Care Explor; 2024 Jul; 6(7):e1116. PubMed ID: 39028867
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Machine Learning-Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study.
    Hur S; Ko RE; Yoo J; Ha J; Cha WC; Chung CR
    JMIR Med Inform; 2021 Jul; 9(7):e23401. PubMed ID: 34309567
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients.
    Shen J; Casie Chetty S; Shokouhi S; Maharjan J; Chuba Y; Calvert J; Mao Q
    Thromb Res; 2022 Aug; 216():14-21. PubMed ID: 35679633
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning].
    Zhang S; Tang S; Rong S; Zhu M; Liu J; Hu Q; Hao C
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Mar; 34(3):255-259. PubMed ID: 35574741
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.