BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

86 related articles for article (PubMed ID: 36265112)

  • 1. Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer.
    Po-Yen Lin F; Salih OSM; Scott N; Jameson MB; Epstein RJ
    JCO Clin Cancer Inform; 2022 Oct; 6():e2200064. PubMed ID: 36265112
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.
    Gupta S; Tran T; Luo W; Phung D; Kennedy RL; Broad A; Campbell D; Kipp D; Singh M; Khasraw M; Matheson L; Ashley DM; Venkatesh S
    BMJ Open; 2014 Mar; 4(3):e004007. PubMed ID: 24643167
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors.
    Zachariah FJ; Rossi LA; Roberts LM; Bosserman LD
    JAMA Netw Open; 2022 May; 5(5):e2214514. PubMed ID: 35639380
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer.
    Manz CR; Chen J; Liu M; Chivers C; Regli SH; Braun J; Draugelis M; Hanson CW; Shulman LN; Schuchter LM; O'Connor N; Bekelman JE; Patel MS; Parikh RB
    JAMA Oncol; 2020 Nov; 6(11):1723-1730. PubMed ID: 32970131
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A prospective, multicenter cohort study to validate a simple performance status-based survival prediction system for oncologists.
    Yamada T; Morita T; Maeda I; Inoue S; Ikenaga M; Matsumoto Y; Baba M; Sekine R; Yamaguchi T; Hirohashi T; Tajima T; Tatara R; Watanabe H; Otani H; Takigawa C; Matsuda Y; Ono S; Ozawa T; Yamamoto R; Shishido H; Yamamoto N
    Cancer; 2017 Apr; 123(8):1442-1452. PubMed ID: 27926777
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.
    Lin H; Long E; Ding X; Diao H; Chen Z; Liu R; Huang J; Cai J; Xu S; Zhang X; Wang D; Chen K; Yu T; Wu D; Zhao X; Liu Z; Wu X; Jiang Y; Yang X; Cui D; Liu W; Zheng Y; Luo L; Wang H; Chan CC; Morgan IG; He M; Liu Y
    PLoS Med; 2018 Nov; 15(11):e1002674. PubMed ID: 30399150
    [TBL] [Abstract][Full Text] [Related]  

  • 7. "How Long Have I Got?"-A Prospective Cohort Study Comparing Validated Prognostic Factors for Use in Patients with Advanced Cancer.
    Simmons C; McMillan DC; Tuck S; Graham C; McKeown A; Bennett M; O'Neill C; Wilcock A; Usborne C; Fearon KC; Fallon M; Laird BJ;
    Oncologist; 2019 Sep; 24(9):e960-e967. PubMed ID: 30975922
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.
    Major VJ; Aphinyanaphongs Y
    BMC Med Inform Decis Mak; 2020 Sep; 20(1):214. PubMed ID: 32894128
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.
    Ribelles N; Jerez JM; Rodriguez-Brazzarola P; Jimenez B; Diaz-Redondo T; Mesa H; Marquez A; Sanchez-Muñoz A; Pajares B; Carabantes F; Bermejo MJ; Villar E; Dominguez-Recio ME; Saez E; Galvez L; Godoy A; Franco L; Ruiz-Medina S; Lopez I; Alba E
    Eur J Cancer; 2021 Feb; 144():224-231. PubMed ID: 33373867
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Clinical narratives as a predictor for prognosticating functional outcomes after intracerebral hemorrhage.
    Hung LC; Su YY; Sun JM; Huang WT; Sung SF
    J Neurol Sci; 2023 Oct; 453():120807. PubMed ID: 37717279
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.
    Kim HJ; Han D; Kim JH; Kim D; Ha B; Seog W; Lee YK; Lim D; Hong SO; Park MJ; Heo J
    J Med Internet Res; 2020 Nov; 22(11):e24225. PubMed ID: 33108316
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records.
    Menger V; Spruit M; van Est R; Nap E; Scheepers F
    JAMA Netw Open; 2019 Jul; 2(7):e196709. PubMed ID: 31268542
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automated model versus treating physician for predicting survival time of patients with metastatic cancer.
    Gensheimer MF; Aggarwal S; Benson KRK; Carter JN; Henry AS; Wood DJ; Soltys SG; Hancock S; Pollom E; Shah NH; Chang DT
    J Am Med Inform Assoc; 2021 Jun; 28(6):1108-1116. PubMed ID: 33313792
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
    Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
    JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models.
    Hindocha S; Charlton TG; Linton-Reid K; Hunter B; Chan C; Ahmed M; Robinson EJ; Orton M; Ahmad S; McDonald F; Locke I; Power D; Blackledge M; Lee RW; Aboagye EO
    EBioMedicine; 2022 Mar; 77():103911. PubMed ID: 35248997
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?
    El-Galaly A; Grazal C; Kappel A; Nielsen PT; Jensen SL; Forsberg JA
    Clin Orthop Relat Res; 2020 Sep; 478(9):2088-2101. PubMed ID: 32667760
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 5.