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 *

153 related articles for article (PubMed ID: 32531520)

  • 1. Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension.
    Datta S; Loftus TJ; Ruppert MM; Giordano C; Upchurch GR; Rashidi P; Ozrazgat-Baslanti T; Bihorac A
    J Surg Res; 2020 Oct; 254():350-363. PubMed ID: 32531520
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Optimizing predictive strategies for acute kidney injury after major vascular surgery.
    Filiberto AC; Ozrazgat-Baslanti T; Loftus TJ; Peng YC; Datta S; Efron P; Upchurch GR; Bihorac A; Cooper MA
    Surgery; 2021 Jul; 170(1):298-303. PubMed ID: 33648766
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.
    Ren Y; Loftus TJ; Datta S; Ruppert MM; Guan Z; Miao S; Shickel B; Feng Z; Giordano C; Upchurch GR; Rashidi P; Ozrazgat-Baslanti T; Bihorac A
    JAMA Netw Open; 2022 May; 5(5):e2211973. PubMed ID: 35576007
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.
    Xue B; Li D; Lu C; King CR; Wildes T; Avidan MS; Kannampallil T; Abraham J
    JAMA Netw Open; 2021 Mar; 4(3):e212240. PubMed ID: 33783520
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.
    Adhikari L; Ozrazgat-Baslanti T; Ruppert M; Madushani RWMA; Paliwal S; Hashemighouchani H; Zheng F; Tao M; Lopes JM; Li X; Rashidi P; Bihorac A
    PLoS One; 2019; 14(4):e0214904. PubMed ID: 30947282
    [TBL] [Abstract][Full Text] [Related]  

  • 6. MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery.
    Bihorac A; Ozrazgat-Baslanti T; Ebadi A; Motaei A; Madkour M; Pardalos PM; Lipori G; Hogan WR; Efron PA; Moore F; Moldawer LL; Wang DZ; Hobson CE; Rashidi P; Li X; Momcilovic P
    Ann Surg; 2019 Apr; 269(4):652-662. PubMed ID: 29489489
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.
    Brennan M; Puri S; Ozrazgat-Baslanti T; Feng Z; Ruppert M; Hashemighouchani H; Momcilovic P; Li X; Wang DZ; Bihorac A
    Surgery; 2019 May; 165(5):1035-1045. PubMed ID: 30792011
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Impact of Intraoperative Data on Risk Prediction for Mortality After Intra-Abdominal Surgery.
    Yan X; Goldsmith J; Mohan S; Turnbull ZA; Freundlich RE; Billings FT; Kiran RP; Li G; Kim M
    Anesth Analg; 2022 Jan; 134(1):102-113. PubMed ID: 34908548
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery.
    Fernandes MPB; Armengol de la Hoz M; Rangasamy V; Subramaniam B
    J Cardiothorac Vasc Anesth; 2021 Mar; 35(3):857-865. PubMed ID: 32747203
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study.
    Bhandari M; Nallabasannagari AR; Reddiboina M; Porter JR; Jeong W; Mottrie A; Dasgupta P; Challacombe B; Abaza R; Rha KH; Parekh DJ; Ahlawat R; Capitanio U; Yuvaraja TB; Rawal S; Moon DA; Buffi NM; Sivaraman A; Maes KK; Porpiglia F; Gautam G; Turkeri L; Meyyazhgan KR; Patil P; Menon M; Rogers C
    BJU Int; 2020 Sep; 126(3):350-358. PubMed ID: 32315504
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Postoperative delirium prediction using machine learning models and preoperative electronic health record data.
    Bishara A; Chiu C; Whitlock EL; Douglas VC; Lee S; Butte AJ; Leung JM; Donovan AL
    BMC Anesthesiol; 2022 Jan; 22(1):8. PubMed ID: 34979919
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.
    Brajer N; Cozzi B; Gao M; Nichols M; Revoir M; Balu S; Futoma J; Bae J; Setji N; Hernandez A; Sendak M
    JAMA Netw Open; 2020 Feb; 3(2):e1920733. PubMed ID: 32031645
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.
    Chiew CJ; Liu N; Wong TH; Sim YE; Abdullah HR
    Ann Surg; 2020 Dec; 272(6):1133-1139. PubMed ID: 30973386
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery.
    Ryan CT; Zeng Z; Chatterjee S; Wall MJ; Moon MR; Coselli JS; Rosengart TK; Li M; Ghanta RK
    J Thorac Cardiovasc Surg; 2023 Dec; 166(6):e551-e564. PubMed ID: 36347651
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A machine learning approach to predict early outcomes after pituitary adenoma surgery.
    Hollon TC; Parikh A; Pandian B; Tarpeh J; Orringer DA; Barkan AL; McKean EL; Sullivan SE
    Neurosurg Focus; 2018 Nov; 45(5):E8. PubMed ID: 30453460
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.
    Laferrière-Langlois P; Imrie F; Geraldo MA; Wingert T; Lahrichi N; van der Schaar M; Cannesson M
    Anesth Analg; 2024 Jul; 139(1):174-185. PubMed ID: 38051671
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development and Validation of an Electronic Health Record-Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment.
    Wong A; Young AT; Liang AS; Gonzales R; Douglas VC; Hadley D
    JAMA Netw Open; 2018 Aug; 1(4):e181018. PubMed ID: 30646095
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning.
    Kim JS; Arvind V; Oermann EK; Kaji D; Ranson W; Ukogu C; Hussain AK; Caridi J; Cho SK
    Spine Deform; 2018; 6(6):762-770. PubMed ID: 30348356
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.
    Abujaber A; Fadlalla A; Gammoh D; Abdelrahman H; Mollazehi M; El-Menyar A
    BMC Med Inform Decis Mak; 2020 Dec; 20(1):336. PubMed ID: 33317528
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep-learning model for predicting 30-day postoperative mortality.
    Fritz BA; Cui Z; Zhang M; He Y; Chen Y; Kronzer A; Ben Abdallah A; King CR; Avidan MS
    Br J Anaesth; 2019 Nov; 123(5):688-695. PubMed ID: 31558311
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.