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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

386 related items for PubMed ID: 27627195

  • 1.
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  • 2. Predicting asthma exacerbations using artificial intelligence.
    Finkelstein J, Wood J.
    Stud Health Technol Inform; 2013; 190():56-8. PubMed ID: 23823374
    [Abstract] [Full Text] [Related]

  • 3. Using machine learning to predict asthma exacerbations.
    Finkelstein J, Gangopadhyay A.
    AMIA Annu Symp Proc; 2007 Oct 11; ():955. PubMed ID: 18694055
    [Abstract] [Full Text] [Related]

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  • 5. Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System.
    Shah SA, Velardo C, Farmer A, Tarassenko L.
    J Med Internet Res; 2017 Mar 07; 19(3):e69. PubMed ID: 28270380
    [Abstract] [Full Text] [Related]

  • 6. Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.
    Ross MK, Yoon J, van der Schaar A, van der Schaar M.
    Ann Am Thorac Soc; 2018 Jan 07; 15(1):49-58. PubMed ID: 29048949
    [Abstract] [Full Text] [Related]

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  • 8. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.
    Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z.
    Comput Methods Programs Biomed; 2020 May 07; 188():105267. PubMed ID: 31841787
    [Abstract] [Full Text] [Related]

  • 9. Fetal health status prediction based on maternal clinical history using machine learning techniques.
    Akbulut A, Ertugrul E, Topcu V.
    Comput Methods Programs Biomed; 2018 Sep 07; 163():87-100. PubMed ID: 30119860
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  • 11. Smooth Bayesian network model for the prediction of future high-cost patients with COPD.
    Lin S, Zhang Q, Chen F, Luo L, Chen L, Zhang W.
    Int J Med Inform; 2019 Jun 07; 126():147-155. PubMed ID: 31029256
    [Abstract] [Full Text] [Related]

  • 12. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.
    Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K.
    Am J Emerg Med; 2018 Sep 07; 36(9):1650-1654. PubMed ID: 29970272
    [Abstract] [Full Text] [Related]

  • 13. Predicting post-stroke pneumonia using deep neural network approaches.
    Ge Y, Wang Q, Wang L, Wu H, Peng C, Wang J, Xu Y, Xiong G, Zhang Y, Yi Y.
    Int J Med Inform; 2019 Dec 07; 132():103986. PubMed ID: 31629312
    [Abstract] [Full Text] [Related]

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  • 15. Telemonitoring in asthma control: a randomized controlled trial.
    Nemanic T, Sarc I, Skrgat S, Flezar M, Cukjati I, Marc Malovrh M.
    J Asthma; 2019 Jul 07; 56(7):782-790. PubMed ID: 30063840
    [Abstract] [Full Text] [Related]

  • 16. Novel Machine Learning Can Predict Acute Asthma Exacerbation.
    Zein JG, Wu CP, Attaway AH, Zhang P, Nazha A.
    Chest; 2021 May 07; 159(5):1747-1757. PubMed ID: 33440184
    [Abstract] [Full Text] [Related]

  • 17. Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation.
    Spyroglou II, Spöck G, Rigas AG, Paraskakis EN.
    BMC Res Notes; 2018 Jul 31; 11(1):522. PubMed ID: 30064478
    [Abstract] [Full Text] [Related]

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  • 19. Application of Machine Learning to Support Self-Management of Asthma with mHealth.
    Tsang KCH, Pinnock H, Wilson AM, Ahmar Shah S.
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul 31; 2020():5673-5677. PubMed ID: 33019264
    [Abstract] [Full Text] [Related]

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