BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1055 related articles for article (PubMed ID: 33955840)

  • 1. Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.
    Wu CT; Li GH; Huang CT; Cheng YC; Chen CH; Chien JY; Kuo PH; Kuo LC; Lai F
    JMIR Mhealth Uhealth; 2021 May; 9(5):e22591. PubMed ID: 33955840
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.
    Wu CT; Wang SM; Su YE; Hsieh TT; Chen PC; Cheng YC; Tseng TW; Chang WS; Su CS; Kuo LC; Chien JY; Lai F
    IEEE J Transl Eng Health Med; 2022; 10():2700414. PubMed ID: 36199984
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.
    Liu JH; Shih CY; Huang HL; Peng JK; Cheng SY; Tsai JS; Lai F
    J Med Internet Res; 2023 Aug; 25():e47366. PubMed ID: 37594793
    [TBL] [Abstract][Full Text] [Related]  

  • 4. [Construction and verification of the risk prediction model for acute exacerbation within 6 months in patients with chronic obstructive pulmonary disease: a secondary analysis based on previous research data].
    Wang M; Cai K; Shi D; Bi L; Li J
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Apr; 34(4):373-377. PubMed ID: 35692201
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients.
    Yin H; Wang K; Yang R; Tan Y; Li Q; Zhu W; Sung S
    Comput Methods Programs Biomed; 2024 Apr; 246():108005. PubMed ID: 38354578
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [Establishment and verification of risk prediction model of acute exacerbation of chronic obstructive pulmonary disease based on regression analysis].
    Wang M; Cai K; Shi D; Tu X; Zhao H; Li S; Li J
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Jan; 33(1):64-68. PubMed ID: 33565403
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data.
    Orchard P; Agakova A; Pinnock H; Burton CD; Sarran C; Agakov F; McKinstry B
    J Med Internet Res; 2018 Sep; 20(9):e263. PubMed ID: 30249589
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD).
    Pépin JL; Degano B; Tamisier R; Viglino D
    Life (Basel); 2022 Mar; 12(4):. PubMed ID: 35454991
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.
    Zeng S; Arjomandi M; Tong Y; Liao ZC; Luo G
    J Med Internet Res; 2022 Jan; 24(1):e28953. PubMed ID: 34989686
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study.
    Chen Z; Wang J; Wang H; Yao Y; Deng H; Peng J; Li X; Wang Z; Chen X; Xiong W; Wang Q; Zhu T
    Front Med (Lausanne); 2023; 10():1105854. PubMed ID: 37056727
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data.
    Fakhraei R; Matelski J; Gershon A; Kendzerska T; Lapointe-Shaw L; Kaneswaran L; Wu R
    COPD; 2023 Dec; 20(1):274-283. PubMed ID: 37555513
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers.
    Huebner ST; Henny S; Giezendanner S; Brack T; Brutsche M; Chhajed P; Clarenbach C; Dieterle T; Egli A; Frey M; Heijnen I; Irani S; Sievi NA; Thurnheer R; Trendelenburg M; Kohler M; Leuppi-Taegtmeyer AB; Leuppi JD
    Respiration; 2022; 101(5):441-454. PubMed ID: 34942619
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.
    Chmiel FP; Burns DK; Pickering JB; Blythin A; Wilkinson TM; Boniface MJ
    JMIR Med Inform; 2022 Mar; 10(3):e26499. PubMed ID: 35311685
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease.
    Kor CT; Li YR; Lin PR; Lin SH; Wang BY; Lin CH
    J Pers Med; 2022 Feb; 12(2):. PubMed ID: 35207716
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study.
    Yin Y; Xu J; Cai S; Chen Y; Chen Y; Li M; Zhang Z; Kang J
    Int J Chron Obstruct Pulmon Dis; 2022; 17():2093-2106. PubMed ID: 36092968
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Risk Assessment of Acute Exacerbation in COPD Patients in the Context of Pulmonary Follow-Up Rehabilitation Based on the Prevalence and Severity of Comorbidities].
    Luu P; Tulka S; Knippschild S; Windisch W; Spielmanns M
    Pneumologie; 2021 Jul; 75(7):516-525. PubMed ID: 33540464
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system.
    Wei S; Lu R; Zhang Z; Wang F; Tan H; Wang X; Ma J; Zhang Y; Deng N; Chen J
    BMC Pulm Med; 2022 Nov; 22(1):438. PubMed ID: 36424599
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Exacerbation predictive modelling using real-world data from the myCOPD app.
    Glyde HMG; Blythin AM; Wilkinson TMA; Nabney IT; Dodd JW
    Heliyon; 2024 May; 10(10):e31201. PubMed ID: 38803869
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations. Developing a Population Surveillance Approach with Administrative Data.
    Tavakoli H; Chen W; Sin DD; FitzGerald JM; Sadatsafavi M
    Ann Am Thorac Soc; 2020 Sep; 17(9):1069-1076. PubMed ID: 32383971
    [No Abstract]   [Full Text] [Related]  

  • 20. Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study.
    de Graaf D; de Vries NM; van de Zande T; Schimmel JJP; Shin S; Kowahl N; Barman P; Kapur R; Marks WJ; van 't Hul A; Bloem B
    JMIR Res Protoc; 2024 May; 13():e55452. PubMed ID: 38713508
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 53.