BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

315 related articles for article (PubMed ID: 37418262)

  • 1. Development and Validation of a Machine Learning Model to Identify Patients Before Surgery at High Risk for Postoperative Adverse Events.
    Mahajan A; Esper S; Oo TH; McKibben J; Garver M; Artman J; Klahre C; Ryan J; Sadhasivam S; Holder-Murray J; Marroquin OC
    JAMA Netw Open; 2023 Jul; 6(7):e2322285. PubMed ID: 37418262
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.
    Abraham VM; Booth G; Geiger P; Balazs GC; Goldman A
    Clin Orthop Relat Res; 2022 Nov; 480(11):2137-2145. PubMed ID: 35767804
    [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. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.
    Corey KM; Kashyap S; Lorenzi E; Lagoo-Deenadayalan SA; Heller K; Whalen K; Balu S; Heflin MT; McDonald SR; Swaminathan M; Sendak M
    PLoS Med; 2018 Nov; 15(11):e1002701. PubMed ID: 30481172
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
    Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
    Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Development and Validation of a Multivariable Prediction Model for Postoperative Intensive Care Unit Stay in a Broad Surgical Population.
    Rozeboom PD; Henderson WG; Dyas AR; Bronsert MR; Colborn KL; Lambert-Kerzner A; Hammermeister KE; McIntyre RC; Meguid RA
    JAMA Surg; 2022 Apr; 157(4):344-352. PubMed ID: 35171216
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery.
    Castela Forte J; Yeshmagambetova G; van der Grinten ML; Scheeren TWL; Nijsten MWN; Mariani MA; Henning RH; Epema AH
    JAMA Netw Open; 2022 Oct; 5(10):e2237970. PubMed ID: 36287565
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.
    Veeravagu A; Li A; Swinney C; Tian L; Moraff A; Azad TD; Cheng I; Alamin T; Hu SS; Anderson RL; Shuer L; Desai A; Park J; Olshen RA; Ratliff JK
    J Neurosurg Spine; 2017 Jul; 27(1):81-91. PubMed ID: 28430052
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and Validation of an Explainable Machine Learning Model for Major Complications After Cytoreductive Surgery.
    Deng H; Eftekhari Z; Carlin C; Veerapong J; Fournier KF; Johnston FM; Dineen SP; Powers BD; Hendrix R; Lambert LA; Abbott DE; Vande Walle K; Grotz TE; Patel SH; Clarke CN; Staley CA; Abdel-Misih S; Cloyd JM; Lee B; Fong Y; Raoof M
    JAMA Netw Open; 2022 May; 5(5):e2212930. PubMed ID: 35612856
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Predictive validity of the ACS-NSQIP surgical risk calculator in geriatric patients undergoing lumbar surgery.
    Wang X; Hu Y; Zhao B; Su Y
    Medicine (Baltimore); 2017 Oct; 96(43):e8416. PubMed ID: 29069040
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions.
    Massaad E; Williams N; Hadzipasic M; Patel SS; Fourman MS; Kiapour A; Schoenfeld AJ; Shankar GM; Shin JH
    Neurosurg Focus; 2021 May; 50(5):E5. PubMed ID: 33932935
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.
    Sun R; Li S; Wei Y; Hu L; Xu Q; Zhan G; Yan X; He Y; Wang Y; Li X; Luo A; Zhou Z
    Int J Surg; 2024 May; 110(5):2950-2962. PubMed ID: 38445452
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.
    Luo XQ; Kang YX; Duan SB; Yan P; Song GB; Zhang NY; Yang SK; Li JX; Zhang H
    J Med Internet Res; 2023 Jan; 25():e41142. PubMed ID: 36603200
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.
    Peng X; Zhu T; Wang T; Wang F; Li K; Hao X
    BMC Anesthesiol; 2022 Sep; 22(1):284. PubMed ID: 36088288
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.
    Banerjee I; Sofela M; Yang J; Chen JH; Shah NH; Ball R; Mushlin AI; Desai M; Bledsoe J; Amrhein T; Rubin DL; Zamanian R; Lungren MP
    JAMA Netw Open; 2019 Aug; 2(8):e198719. PubMed ID: 31390040
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study.
    Wong DJN; Harris S; Sahni A; Bedford JR; Cortes L; Shawyer R; Wilson AM; Lindsay HA; Campbell D; Popham S; Barneto LM; Myles PS; ; Moonesinghe SR
    PLoS Med; 2020 Oct; 17(10):e1003253. PubMed ID: 33057333
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study.
    Bonde A; Varadarajan KM; Bonde N; Troelsen A; Muratoglu OK; Malchau H; Yang AD; Alam H; Sillesen M
    Lancet Digit Health; 2021 Aug; 3(8):e471-e485. PubMed ID: 34215564
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016.
    van den Bosch T; Warps AK; de Nerée Tot Babberich MPM; Stamm C; Geerts BF; Vermeulen L; Wouters MWJM; Dekker JWT; Tollenaar RAEM; Tanis PJ; Miedema DM;
    JAMA Netw Open; 2021 Apr; 4(4):e217737. PubMed ID: 33900400
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.