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 *

167 related articles for article (PubMed ID: 37225754)

  • 21. Epidemiology of type 1 and type 2 diabetes mellitus in Kazakhstan: data from unified National Electronic Health System 2014-2019.
    Galiyeva D; Gusmanov A; Sakko Y; Issanov A; Atageldiyeva K; Kadyrzhanuly K; Nurpeissova A; Rakhimzhanova M; Durmanova A; Sarria-Santamera A; Gaipov A
    BMC Endocr Disord; 2022 Nov; 22(1):275. PubMed ID: 36368961
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.
    Khadem H; Nemat H; Elliott J; Benaissa M
    Sensors (Basel); 2022 Nov; 22(22):. PubMed ID: 36433354
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality.
    Kanwal F; Taylor TJ; Kramer JR; Cao Y; Smith D; Gifford AL; El-Serag HB; Naik AD; Asch SM
    JAMA Netw Open; 2020 Nov; 3(11):e2023780. PubMed ID: 33141161
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Predictive models for diabetes mellitus using machine learning techniques.
    Lai H; Huang H; Keshavjee K; Guergachi A; Gao X
    BMC Endocr Disord; 2019 Oct; 19(1):101. PubMed ID: 31615566
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
    Zou Y; Zhao L; Zhang J; Wang Y; Wu Y; Ren H; Wang T; Zhang R; Wang J; Zhao Y; Qin C; Xu H; Li L; Chai Z; Cooper ME; Tong N; Liu F
    Ren Fail; 2022 Dec; 44(1):562-570. PubMed ID: 35373711
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis.
    Yu J; Chiu C; Wang Y; Dzubur E; Lu W; Hoffman J
    J Med Internet Res; 2021 Aug; 23(8):e27709. PubMed ID: 34448707
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach.
    Wong KC; Xiang Y; Yin L; So HC
    JMIR Public Health Surveill; 2021 Sep; 7(9):e29544. PubMed ID: 34591027
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests.
    Meiseles A; Paley D; Ziv M; Hadid Y; Rokach L; Tadmor T
    Comput Biol Med; 2022 Jun; 145():105490. PubMed ID: 35405402
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Application of machine learning techniques for predicting survival in ovarian cancer.
    Sorayaie Azar A; Babaei Rikan S; Naemi A; Bagherzadeh Mohasefi J; Pirnejad H; Bagherzadeh Mohasefi M; Wiil UK
    BMC Med Inform Decis Mak; 2022 Dec; 22(1):345. PubMed ID: 36585641
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.
    Ikemura K; Bellin E; Yagi Y; Billett H; Saada M; Simone K; Stahl L; Szymanski J; Goldstein DY; Reyes Gil M
    J Med Internet Res; 2021 Feb; 23(2):e23458. PubMed ID: 33539308
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018.
    Lin Z; Cheng YT; Cheung BMY
    Ann Med; 2023 Dec; 55(1):2209336. PubMed ID: 37162442
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations.
    Dong S; Khattak A; Ullah I; Zhou J; Hussain A
    Int J Environ Res Public Health; 2022 Mar; 19(5):. PubMed ID: 35270617
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. 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]  

  • 35. Development of Various Diabetes Prediction Models Using Machine Learning Techniques.
    Shin J; Kim J; Lee C; Yoon JY; Kim S; Song S; Kim HS
    Diabetes Metab J; 2022 Jul; 46(4):650-657. PubMed ID: 35272434
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.
    Hu CA; Chen CM; Fang YC; Liang SJ; Wang HC; Fang WF; Sheu CC; Perng WC; Yang KY; Kao KC; Wu CL; Tsai CS; Lin MY; Chao WC;
    BMJ Open; 2020 Feb; 10(2):e033898. PubMed ID: 32102816
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images.
    Kim YD; Noh KJ; Byun SJ; Lee S; Kim T; Sunwoo L; Lee KJ; Kang SH; Park KH; Park SJ
    Sci Rep; 2020 Mar; 10(1):4623. PubMed ID: 32165702
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Interpretable prediction of mortality in liver transplant recipients based on machine learning.
    Zhang X; Gavaldà R; Baixeries J
    Comput Biol Med; 2022 Dec; 151(Pt A):106188. PubMed ID: 36306583
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods.
    Bogale DS; Abuhay TM; Dejene BE
    BMC Med Inform Decis Mak; 2022 Dec; 22(1):341. PubMed ID: 36577978
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.
    Long Z; Yi M; Qin Y; Ye Q; Che X; Wang S; Lei M
    Front Oncol; 2023; 13():1144039. PubMed ID: 36890826
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.