IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v263y2018i1d10.1007_s10479-017-2489-0.html
   My bibliography  Save this article

Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network

Author

Listed:
  • Kazim Topuz

    (Wichita State University)

  • Hasmet Uner

    (Kansas University School of Medicine)

  • Asil Oztekin

    (University of Massachusetts Lowell)

  • Mehmet Bayram Yildirim

    (Wichita State University)

Abstract

No-shows are becoming a major problem in primary care facilities, creating additional costs for the facility while adversely affecting the quality of patient care. Accurately predicting no-shows plays an important role in the overbooking strategy. In this study, a hybrid probabilistic prediction framework based on the elastic net (EN) variable-selection methodology integrated with probabilistic Bayesian Belief Network (BBN) is proposed. The study predicts the “no-show probability of the patient(s)” using demographics, socioeconomic status, current appointment information, and appointment attendance history of the patient and the family. The proposed framework is validated using ten years of local pediatric clinic data. It is shown that this EN-based BBN framework is a comparable prediction methodology regarding the best approaches found in the literature. More importantly, this methodology provides novel information on the interrelations of predictors and the conditional probability of predicting “no-shows.” The output of the model can be applied to the appointment scheduling system for a robust overbooking strategy.

Suggested Citation

  • Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2489-0
    DOI: 10.1007/s10479-017-2489-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2489-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2489-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    2. Uwe Aickelin & Jingpeng Li, 2007. "An estimation of distribution algorithm for nurse scheduling," Annals of Operations Research, Springer, vol. 155(1), pages 289-309, November.
    3. Hsinchun Chen & Ganesan Shankaranarayanan & Linlin She & Anand Iyer, 1998. "A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(8), pages 693-705.
    4. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    5. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, October.
    6. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    7. Clyde Holsapple & Anita Lee & Jim Otto, 1997. "A machine learning method for multi-expert decision support," Annals of Operations Research, Springer, vol. 75(0), pages 171-188, January.
    8. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
    9. Abramson, Bruce & Brown, John & Edwards, Ward & Murphy, Allan & Winkler, Robert L., 1996. "Hailfinder: A Bayesian system for forecasting severe weather," International Journal of Forecasting, Elsevier, vol. 12(1), pages 57-71, March.
    10. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    11. Anders Kristensen & Erik Jørgensen, 2000. "Multi‐level hierarchic Markov processes as a framework for herd management support," Annals of Operations Research, Springer, vol. 94(1), pages 69-89, January.
    12. Haipeng Guo & William Hsu, 2007. "A machine learning approach to algorithm selection for $\mathcal{NP}$ -hard optimization problems: a case study on the MPE problem," Annals of Operations Research, Springer, vol. 156(1), pages 61-82, December.
    13. Suits, Daniel B, 1984. "Dummy Variables: Mechanics v. Interpretation," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 177-180, February.
    14. K J Glowacka & R M Henry & J H May, 2009. "A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1056-1068, August.
    15. Hsinchun Chen, 2003. "Introduction to the JASIST Special Topic issue on web retrieval and mining: A machine learning perspective," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(7), pages 621-624, May.
    16. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    17. Adel Alaeddini & Kai Yang & Chandan Reddy & Susan Yu, 2011. "A probabilistic model for predicting the probability of no-show in hospital appointments," Health Care Management Science, Springer, vol. 14(2), pages 146-157, June.
    18. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mi Young Suk & Bomgyeol Kim & Sang Gyu Lee & Chang Hoon You & Tae Hyun Kim, 2021. "Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    2. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    3. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    4. Borges, Ana & Carvalho, Mariana & Maia, Miguel & Guimarães, Miguel & Carneiro, Davide, 2023. "Predicting and explaining absenteeism risk in hospital patients before and during COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    5. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
    6. Dominik Schreyer & Sascha L. Schmidt & Benno Torgler, 2019. "Football Spectator No-Show Behavior," Journal of Sports Economics, , vol. 20(4), pages 580-602, May.
    7. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    8. J. Dunstan & F. Villena & J.P. Hoyos & V. Riquelme & M. Royer & H. Ramírez & J. Peypouquet, 2023. "Predicting no-show appointments in a pediatric hospital in Chile using machine learning," Health Care Management Science, Springer, vol. 26(2), pages 313-329, June.
    9. Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    10. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    11. Cheng Wang & Runhua Wu & Lili Deng & Yong Chen & Yingde Li & Yuehua Wan, 2020. "A Bibliometric Analysis on No-Show Research: Status, Hotspots, Trends and Outlook," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    12. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).
    13. Rukiye Kaya & Said Salhi & Virginia Spiegler, 2023. "A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge," Annals of Operations Research, Springer, vol. 320(1), pages 205-234, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    2. Gilles Charmet & Louis-Gautier Tran & Jérôme Auzanneau & Renaud Rincent & Sophie Bouchet, 2020. "BWGS: A R package for genomic selection and its application to a wheat breeding programme," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    3. Scutari Marco & Mackay Ian & Balding David, 2013. "Improving the efficiency of genomic selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 517-527, August.
    4. Posch, Konstantin & Arbeiter, Maximilian & Pilz, Juergen, 2020. "A novel Bayesian approach for variable selection in linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    5. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    6. van Erp, Sara & Oberski, Daniel L. & Mulder, Joris, 2018. "Shrinkage priors for Bayesian penalized regression," OSF Preprints cg8fq, Center for Open Science.
    7. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    8. Shutes, Karl & Adcock, Chris, 2013. "Regularized Skew-Normal Regression," MPRA Paper 52217, University Library of Munich, Germany, revised 11 Dec 2013.
    9. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Bayesian model selection for generalized linear models using non-local priors," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 285-296.
    10. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    11. Shutes, Karl & Adcock, Chris, 2013. "Regularized Extended Skew-Normal Regression," MPRA Paper 58445, University Library of Munich, Germany, revised 09 Sep 2014.
    12. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.
    13. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.
    14. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    15. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    16. Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow, 2022. "Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 973-996, July.
    17. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    18. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    19. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    20. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2489-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.