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Journal Abstract Search

880 related items for PubMed ID: 33099184

  • 1. Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: A data analytics approach.
    Srinivas S, Salah H.
    Int J Med Inform; 2021 Jan; 145():104290. PubMed ID: 33099184
    [Abstract] [Full Text] [Related]

  • 2. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models.
    Babayoff O, Shehory O, Geller S, Shitrit-Niselbaum C, Weiss-Meilik A, Sprecher E.
    J Med Syst; 2022 Dec 31; 47(1):5. PubMed ID: 36585996
    [Abstract] [Full Text] [Related]

  • 3. Efficient Prediction of Missed Clinical Appointment Using Machine Learning.
    Qureshi Z, Maqbool A, Mirza A, Iqbal MZ, Afzal F, Kanubala DD, Rana T, Umair MY, Wakeel A, Shah SK.
    Comput Math Methods Med; 2021 Dec 31; 2021():2376391. PubMed ID: 34721656
    [Abstract] [Full Text] [Related]

  • 4. Data Analytics and Modeling for Appointment No-show in Community Health Centers.
    Mohammadi I, Wu H, Turkcan A, Toscos T, Doebbeling BN.
    J Prim Care Community Health; 2018 Dec 31; 9():2150132718811692. PubMed ID: 30451063
    [Abstract] [Full Text] [Related]

  • 5. A Decision-Making tool based on historical data for service time prediction in outpatient scheduling.
    Golmohammadi D.
    Int J Med Inform; 2021 Dec 31; 156():104591. PubMed ID: 34638011
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  • 6. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.
    Gopukumar D, Ghoshal A, Zhao H.
    JMIR Med Inform; 2022 Aug 30; 10(8):e37578. PubMed ID: 35896038
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  • 7. Deep convolutional neural network and IoT technology for healthcare.
    Wassan S, Dongyan H, Suhail B, Jhanjhi NZ, Xiao G, Ahmed S, Murugesan RK.
    Digit Health; 2024 Aug 30; 10():20552076231220123. PubMed ID: 38250147
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  • 8. Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high.
    Barghash M, Saleet H.
    Int J Health Care Qual Assur; 2018 May 14; 31(4):309-326. PubMed ID: 29790448
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  • 9. Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model.
    Kigo SN, Omondi EO, Omolo BO.
    Sci Rep; 2023 Oct 12; 13(1):17315. PubMed ID: 37828360
    [Abstract] [Full Text] [Related]

  • 10. Design of appointment systems for preanesthesia evaluation clinics to minimize patient waiting times: a review of computer simulation and patient survey studies.
    Dexter F.
    Anesth Analg; 1999 Oct 12; 89(4):925-31. PubMed ID: 10512266
    [Abstract] [Full Text] [Related]

  • 11. Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics.
    Cuevas-Nunez M, Pan A, Sangalli L, Haering HJ, Mitchell JC.
    J Dent Educ; 2023 Dec 12; 87(12):1735-1745. PubMed ID: 37786254
    [Abstract] [Full Text] [Related]

  • 12. Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye.
    Demir Yetiş A, İlhan N, Kara H.
    Environ Sci Pollut Res Int; 2024 Jul 12; 31(34):47201-47219. PubMed ID: 38990257
    [Abstract] [Full Text] [Related]

  • 13. Emergency department triage prediction of clinical outcomes using machine learning models.
    Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K.
    Crit Care; 2019 Feb 22; 23(1):64. PubMed ID: 30795786
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  • 14. Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers.
    Srinivas S, Ravindran AR.
    Health Care Manag Sci; 2020 Sep 22; 23(3):360-386. PubMed ID: 32078081
    [Abstract] [Full Text] [Related]

  • 15. 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 22; 132():103986. PubMed ID: 31629312
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  • 16. Coordinating clinic and surgery appointments to meet access service levels for elective surgery.
    Kazemian P, Sir MY, Van Oyen MP, Lovely JK, Larson DW, Pasupathy KS.
    J Biomed Inform; 2017 Feb 22; 66():105-115. PubMed ID: 27993748
    [Abstract] [Full Text] [Related]

  • 17. A dynamic approach for outpatient scheduling.
    Creps J, Lotfi V.
    J Med Econ; 2017 Aug 22; 20(8):786-798. PubMed ID: 28402208
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  • 18. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.
    Huang JC, Tsai YC, Wu PY, Lien YH, Chien CY, Kuo CF, Hung JF, Chen SC, Kuo CH.
    Comput Methods Programs Biomed; 2020 Oct 22; 195():105536. PubMed ID: 32485511
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  • 19. Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.
    Butt FM, Hussain L, Mahmood A, Lone KJ.
    Math Biosci Eng; 2020 Dec 04; 18(1):400-425. PubMed ID: 33525099
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  • 20. Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension.
    Barsasella D, Gupta S, Malwade S, Aminin, Susanti Y, Tirmadi B, Mutamakin A, Jonnagaddala J, Syed-Abdul S.
    Int J Med Inform; 2021 Oct 04; 154():104569. PubMed ID: 34525441
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