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 *

132 related articles for article (PubMed ID: 31849535)

  • 21. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models.
    Liao W; Coupland CAC; Burchardt J; Baldwin DR; ; Gleeson FV; Hippisley-Cox J
    Lancet Respir Med; 2023 Aug; 11(8):685-697. PubMed ID: 37030308
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection.
    Zeng J; Zeng J; Lin K; Lin H; Wu Q; Guo P; Zhou W; Liu J
    Hepatobiliary Surg Nutr; 2022 Apr; 11(2):176-187. PubMed ID: 35464276
    [TBL] [Abstract][Full Text] [Related]  

  • 23. A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation.
    Peng W; Jiang X; Zhang W; Hu J; Zhang Y; Zhang L
    Asian J Surg; 2023 Jul; 46(7):2689-2696. PubMed ID: 36351862
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model.
    Zhou J; Chou OHI; Wong KHG; Lee S; Leung KSK; Liu T; Cheung BMY; Wong ICK; Tse G; Zhang Q
    Front Cardiovasc Med; 2022; 9():735906. PubMed ID: 35872897
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.
    Dietrich S; Floegel A; Troll M; Kühn T; Rathmann W; Peters A; Sookthai D; von Bergen M; Kaaks R; Adamski J; Prehn C; Boeing H; Schulze MB; Illig T; Pischon T; Knüppel S; Wang-Sattler R; Drogan D
    Int J Epidemiol; 2016 Oct; 45(5):1406-1420. PubMed ID: 27591264
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery.
    Li Y; Chen M; Lv H; Yin P; Zhang L; Tang P
    Injury; 2021 Jun; 52(6):1487-1493. PubMed ID: 33386157
    [TBL] [Abstract][Full Text] [Related]  

  • 27. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
    Liu Y; Zhou S; Wei H; An S
    BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information.
    Dykstra S; Satriano A; Cornhill AK; Lei LY; Labib D; Mikami Y; Flewitt J; Rivest S; Sandonato R; Feuchter P; Howarth AG; Lydell CP; Fine NM; Exner DV; Morillo CA; Wilton SB; Gavrilova ML; White JA
    Front Cardiovasc Med; 2022; 9():998558. PubMed ID: 36247426
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.
    Kar İ; Kocaman G; İbrahimov F; Enön S; Coşgun E; Elhan AH
    Cancer Med; 2023 Sep; 12(18):19272-19278. PubMed ID: 37644818
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.
    Segar MW; Vaduganathan M; Patel KV; McGuire DK; Butler J; Fonarow GC; Basit M; Kannan V; Grodin JL; Everett B; Willett D; Berry J; Pandey A
    Diabetes Care; 2019 Dec; 42(12):2298-2306. PubMed ID: 31519694
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Machine Learning-Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data.
    Bao L; Wang YT; Zhuang JL; Liu AJ; Dong YJ; Chu B; Chen XH; Lu MQ; Shi L; Gao S; Fang LJ; Xiang QQ; Ding YH
    Front Oncol; 2022; 12():922039. PubMed ID: 35865475
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Identifying important risk factors for survival in patient with systolic heart failure using random survival forests.
    Hsich E; Gorodeski EZ; Blackstone EH; Ishwaran H; Lauer MS
    Circ Cardiovasc Qual Outcomes; 2011 Jan; 4(1):39-45. PubMed ID: 21098782
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests.
    Hamidi O; Poorolajal J; Farhadian M; Tapak L
    Iran J Public Health; 2016 Jan; 45(1):27-33. PubMed ID: 27057518
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Development of a cardiovascular diseases risk prediction model and tools for Chinese patients with type 2 diabetes mellitus: A population-based retrospective cohort study.
    Wan EYF; Fong DYT; Fung CSC; Yu EYT; Chin WY; Chan AKC; Lam CLK
    Diabetes Obes Metab; 2018 Feb; 20(2):309-318. PubMed ID: 28722290
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest.
    Akai H; Yasaka K; Kunimatsu A; Nojima M; Kokudo T; Kokudo N; Hasegawa K; Abe O; Ohtomo K; Kiryu S
    Diagn Interv Imaging; 2018 Oct; 99(10):643-651. PubMed ID: 29910166
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.
    Byun SS; Heo TS; Choi JM; Jeong YS; Kim YS; Lee WK; Kim C
    Sci Rep; 2021 Jan; 11(1):1242. PubMed ID: 33441830
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study.
    Hippisley-Cox J; Coupland C
    BMJ; 2017 Nov; 359():j5019. PubMed ID: 29158232
    [No Abstract]   [Full Text] [Related]  

  • 39. Development and validation of the EHS-COPD model to predict sex-specific risk of chronic obstructive pulmonary disease (COPD) in older Chinese adults: Hong Kong's Elderly Health Service Cohort.
    Yang Z; Schooling CM; Lee SY; Kwok MK
    Ann Transl Med; 2022 Jan; 10(1):4. PubMed ID: 35242849
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests.
    Chen Y; Li G; Jiang W; Nie RC; Deng H; Chen Y; Li H; Chen Y
    Cancer Med; 2023 May; 12(9):10899-10907. PubMed ID: 36934429
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.