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 *

192 related articles for article (PubMed ID: 30643187)

  • 21. Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning.
    Ayers B; Wood K; Gosev I; Prasad S
    Ann Thorac Surg; 2020 Oct; 110(4):1193-1200. PubMed ID: 32454016
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.
    Li J; Chen Q; Hu X; Yuan P; Cui L; Tu L; Cui J; Huang J; Jiang T; Ma X; Yao X; Zhou C; Lu H; Xu J
    Int J Med Inform; 2021 May; 149():104429. PubMed ID: 33647600
    [TBL] [Abstract][Full Text] [Related]  

  • 23. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.
    Nemati S; Holder A; Razmi F; Stanley MD; Clifford GD; Buchman TG
    Crit Care Med; 2018 Apr; 46(4):547-553. PubMed ID: 29286945
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.
    Lei M; Han Z; Wang S; Guo C; Zhang X; Song Y; Lin F; Huang T
    Front Immunol; 2022; 13():979877. PubMed ID: 36325351
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.
    Stylianou N; Akbarov A; Kontopantelis E; Buchan I; Dunn KW
    Burns; 2015 Aug; 41(5):925-34. PubMed ID: 25931158
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.
    Sánchez Fernández I; Sansevere AJ; Gaínza-Lein M; Kapur K; Loddenkemper T
    J Child Neurol; 2018 Jul; 33(8):546-553. PubMed ID: 29756499
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. Intra-operative temperature monitoring with cutaneous zero-heat- flux-thermometry in comparison with oesophageal temperature: A prospective study in the paediatric population.
    Carvalho H; Najafi N; Poelaert J
    Paediatr Anaesth; 2019 Aug; 29(8):865-871. PubMed ID: 31034706
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.
    Ghosh S; Li J; Cao L; Ramamohanarao K
    J Biomed Inform; 2017 Feb; 66():19-31. PubMed ID: 28011233
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Randomized evaluation of fluid resuscitation with crystalloid (saline) and colloid (polymer from degraded gelatin in saline) in pediatric septic shock.
    Upadhyay M; Singhi S; Murlidharan J; Kaur N; Majumdar S
    Indian Pediatr; 2005 Mar; 42(3):223-31. PubMed ID: 15817970
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Early differentiation between dengue and septic shock by comparison of admission hemodynamic, clinical, and laboratory variables: a pilot study.
    Ranjit S; Kissoon N; Gandhi D; Dayal A; Rajeshwari N; Kamath SR
    Pediatr Emerg Care; 2007 Jun; 23(6):368-75. PubMed ID: 17572519
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Performance of the Obstetric Early Warning Score in critically ill patients for the prediction of maternal death.
    Paternina-Caicedo A; Miranda J; Bourjeily G; Levinson A; Dueñas C; Bello-Muñoz C; Rojas-Suarez JA
    Am J Obstet Gynecol; 2017 Jan; 216(1):58.e1-58.e8. PubMed ID: 27751799
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Clinical picture and risk prediction of short-term mortality in cardiogenic shock.
    Harjola VP; Lassus J; Sionis A; Køber L; Tarvasmäki T; Spinar J; Parissis J; Banaszewski M; Silva-Cardoso J; Carubelli V; Di Somma S; Tolppanen H; Zeymer U; Thiele H; Nieminen MS; Mebazaa A; ;
    Eur J Heart Fail; 2015 May; 17(5):501-9. PubMed ID: 25820680
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.
    Kickingereder P; Bonekamp D; Nowosielski M; Kratz A; Sill M; Burth S; Wick A; Eidel O; Schlemmer HP; Radbruch A; Debus J; Herold-Mende C; Unterberg A; Jones D; Pfister S; Wick W; von Deimling A; Bendszus M; Capper D
    Radiology; 2016 Dec; 281(3):907-918. PubMed ID: 27636026
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.
    Mao B; Zhang L; Ning P; Ding F; Wu F; Lu G; Geng Y; Ma J
    Eur Radiol; 2020 Dec; 30(12):6924-6932. PubMed ID: 32696256
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Hemodynamic variables and mortality in cardiogenic shock: a retrospective cohort study.
    Torgersen C; Schmittinger CA; Wagner S; Ulmer H; Takala J; Jakob SM; Dünser MW
    Crit Care; 2009; 13(5):R157. PubMed ID: 19799772
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Machine learning models in breast cancer survival prediction.
    Montazeri M; Montazeri M; Montazeri M; Beigzadeh A
    Technol Health Care; 2016; 24(1):31-42. PubMed ID: 26409558
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
    Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK
    Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.
    Churpek MM; Yuen TC; Winslow C; Meltzer DO; Kattan MW; Edelson DP
    Crit Care Med; 2016 Feb; 44(2):368-74. PubMed ID: 26771782
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.
    Rahimian F; Salimi-Khorshidi G; Payberah AH; Tran J; Ayala Solares R; Raimondi F; Nazarzadeh M; Canoy D; Rahimi K
    PLoS Med; 2018 Nov; 15(11):e1002695. PubMed ID: 30458006
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.