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 *

372 related articles for article (PubMed ID: 30094049)

  • 1. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data.
    Mohamadlou H; Lynn-Palevsky A; Barton C; Chettipally U; Shieh L; Calvert J; Saber NR; Das R
    Can J Kidney Health Dis; 2018; 5():2054358118776326. PubMed ID: 30094049
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study.
    Persson I; Grünwald A; Morvan L; Becedas D; Arlbrandt M
    JMIR Form Res; 2023 Dec; 7():e45979. PubMed ID: 38096015
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.
    Le S; Allen A; Calvert J; Palevsky PM; Braden G; Patel S; Pellegrini E; Green-Saxena A; Hoffman J; Das R
    Kidney Int Rep; 2021 May; 6(5):1289-1298. PubMed ID: 34013107
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.
    Churpek MM; Carey KA; Edelson DP; Singh T; Astor BC; Gilbert ER; Winslow C; Shah N; Afshar M; Koyner JL
    JAMA Netw Open; 2020 Aug; 3(8):e2012892. PubMed ID: 32780123
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
    Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
    Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
    [No Abstract]   [Full Text] [Related]  

  • 6. Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.
    Hsu CN; Liu CL; Tain YL; Kuo CY; Lin YC
    J Med Internet Res; 2020 Aug; 22(8):e16903. PubMed ID: 32749223
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.
    Desautels T; Calvert J; Hoffman J; Jay M; Kerem Y; Shieh L; Shimabukuro D; Chettipally U; Feldman MD; Barton C; Wales DJ; Das R
    JMIR Med Inform; 2016 Sep; 4(3):e28. PubMed ID: 27694098
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Acute kidney injury risk prediction score for critically-ill surgical patients.
    Trongtrakul K; Patumanond J; Kongsayreepong S; Morakul S; Pipanmekaporn T; Akaraborworn O; Poopipatpab S
    BMC Anesthesiol; 2020 Jun; 20(1):140. PubMed ID: 32493268
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model.
    Alfieri F; Ancona A; Tripepi G; Rubeis A; Arjoldi N; Finazzi S; Cauda V; Fagugli RM
    PLoS One; 2023; 18(7):e0287398. PubMed ID: 37490482
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.
    Wu L; Hu Y; Zhang X; Chen W; Yu ASL; Kellum JA; Waitman LR; Liu M
    BMC Nephrol; 2020 Aug; 21(1):321. PubMed ID: 32741377
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
    Dong J; Feng T; Thapa-Chhetry B; Cho BG; Shum T; Inwald DP; Newth CJL; Vaidya VU
    Crit Care; 2021 Aug; 25(1):288. PubMed ID: 34376222
    [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. Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients.
    Nikkinen O; Kolehmainen T; Aaltonen T; Jämsä E; Alahuhta S; Vakkala M
    Comput Biol Med; 2022 May; 144():105351. PubMed ID: 35286890
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Congestive Heart Failure.
    Peng X; Li L; Wang X; Zhang H
    Front Cardiovasc Med; 2022; 9():842873. PubMed ID: 35310995
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression.
    Zhao X; Lu Y; Li S; Guo F; Xue H; Jiang L; Wang Z; Zhang C; Xie W; Zhu F
    Ren Fail; 2022 Dec; 44(1):1326-1337. PubMed ID: 35930309
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.
    Sandokji I; Yamamoto Y; Biswas A; Arora T; Ugwuowo U; Simonov M; Saran I; Martin M; Testani JM; Mansour S; Moledina DG; Greenberg JH; Wilson FP
    J Am Soc Nephrol; 2020 Jun; 31(6):1348-1357. PubMed ID: 32381598
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A risk prediction score for acute kidney injury in the intensive care unit.
    Malhotra R; Kashani KB; Macedo E; Kim J; Bouchard J; Wynn S; Li G; Ohno-Machado L; Mehta R
    Nephrol Dial Transplant; 2017 May; 32(5):814-822. PubMed ID: 28402551
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.
    Zhou H; Liu L; Zhao Q; Jin X; Peng Z; Wang W; Huang L; Xie Y; Xu H; Tao L; Xiao X; Nie W; Liu F; Li L; Yuan Q
    Front Immunol; 2023; 14():1140755. PubMed ID: 37077912
    [TBL] [Abstract][Full Text] [Related]  

  • 19. [Renal echography and cystatin C for prediction of acute kidney injury: very different in patients with cardiac failure or sepsis].
    Zhi H; Zhang M; Cui X; Li Y
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2019 Oct; 31(10):1258-1263. PubMed ID: 31771725
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study.
    Chua HR; Zheng K; Vathsala A; Ngiam KY; Yap HK; Lu L; Tiong HY; Mukhopadhyay A; MacLaren G; Lim SL; Akalya K; Ooi BC
    J Med Internet Res; 2021 Dec; 23(12):e30805. PubMed ID: 34951595
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.