Skip Navigation
Skip to contents

JPMPH : Journal of Preventive Medicine and Public Health

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
1 "Eun Whan Lee"
Filter
Filter
Article category
Keywords
Publication year
Authors
Original Article
Selecting the Best Prediction Model for Readmission
Eun Whan Lee
J Prev Med Public Health. 2012;45(4):259-266.   Published online July 31, 2012
DOI: https://doi.org/10.3961/jpmph.2012.45.4.259
  • 12,200 View
  • 104 Download
  • 34 Crossref
AbstractAbstract PDF
Objectives

This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model.

Methods

In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve.

Results

The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.

Conclusions

When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

Summary

Citations

Citations to this article as recorded by  
  • Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach
    Xiaoquan Gao, Sabriya Alam, Pengyi Shi, Franklin Dexter, Nan Kong
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
  • Burden and patient characteristics associated with repeat consultation for unscheduled care within 30 days in primary care: a retrospective case control study with implications for aging and public health
    Valentin Richard, Leila Bouazzi, Clément Richard, Stéphane Sanchez
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare
    Somya D. Mohanty, Deborah Lekan, Thomas P. McCoy, Marjorie Jenkins, Prashanti Manda
    Patterns.2022; 3(1): 100395.     CrossRef
  • AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge
    Jiin-Chyr Hsu, Fu-Hsing Wu, Hsuan-Hung Lin, Dah-Jye Lee, Yung-Fu Chen, Chih-Sheng Lin
    Electronics.2022; 11(5): 673.     CrossRef
  • An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients
    Lin Wang, Guihua Li, Chika F. Ezeana, Richard Ogunti, Mamta Puppala, Tiancheng He, Xiaohui Yu, Solomon S. Y. Wong, Zheng Yin, Aaron W. Roberts, Aryan Nezamabadi, Pingyi Xu, Adaani Frost, Robert E. Jackson, Stephen T. C. Wong
    Scientific Reports.2022;[Epub]     CrossRef
  • Medicare cost reduction in the US: A case study of hospital readmissions and value-based purchasing
    Mehmet C. Kocakulah, David Austill, Eric Henderson
    International Journal of Healthcare Management.2021; 14(1): 203.     CrossRef
  • Published models that predict hospital readmission: a critical appraisal
    Lisa Grossman Liu, James R Rogers, Rollin Reeder, Colin G Walsh, Devan Kansagara, David K Vawdrey, Hojjat Salmasian
    BMJ Open.2021; 11(8): e044964.     CrossRef
  • Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers
    Nor Hamizah Miswan, Chee Seng Chan, Chong Guan Ng
    Intelligent Data Analysis.2021; 25(5): 1073.     CrossRef
  • Designing a clinical decision support system to predict readmissions for patients admitted with all-cause conditions
    Huey-Jen Lai, Tan-Hsu Tan, Chih-Sheng Lin, Yung-Fu Chen, Hsuan-Hung Lin
    Journal of Ambient Intelligence and Humanized Computing.2020;[Epub]     CrossRef
  • Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital
    Santiago Romero-Brufau, Kirk D. Wyatt, Patricia Boyum, Mindy Mickelson, Matthew Moore, Cheristi Cognetta-Rieke
    Applied Clinical Informatics.2020; 11(04): 570.     CrossRef
  • Independent prospective validation of a medication‐based 15‐day readmission risk stratification algorithm in a tertiary acute care hospital
    Denise Yeo, T. W. Chew, Y. F. Lai
    JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY.2019; 2(1): 40.     CrossRef
  • Risk Assessment of Acute, All-Cause 30-Day Readmission in Patients Aged 65+: a Nationwide, Register-Based Cohort Study
    Mona K. Pedersen, Gunnar L. Nielsen, Lisbeth Uhrenfeldt, Søren Lundbye-Christensen
    Journal of General Internal Medicine.2019; 34(2): 226.     CrossRef
  • A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records
    Alvaro Ribeiro Botelho Junqueira, Farhaan Mirza, Mirza Mansoor Baig
    Health and Technology.2019; 9(3): 297.     CrossRef
  • Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients
    Anne-Laure Blanc, Thierry Fumeaux, Jérôme Stirnemann, Elise Dupuis Lozeron, Aimad Ourhamoune, Jules Desmeules, Pierre Chopard, Arnaud Perrier, Nicolas Schaad, Pascal Bonnabry, Enrico Mossello
    PLOS ONE.2019; 14(7): e0219348.     CrossRef
  • An integrated machine learning framework for hospital readmission prediction
    Shancheng Jiang, Kwai-Sang Chin, Gang Qu, Kwok L. Tsui
    Knowledge-Based Systems.2018; 146: 73.     CrossRef
  • Development and prospective validation of a model estimating risk of readmission in cancer patients
    Carl R. Schmidt, Jennifer Hefner, Ann S. McAlearney, Lisa Graham, Kristen Johnson, Susan Moffatt‐Bruce, Timothy Huerta, Timothy M. Pawlik, Susan White
    Journal of Surgical Oncology.2018; 117(6): 1113.     CrossRef
  • Characterization, Categorization, and 5-Year Mortality of Medicine High Utilizer Inpatients
    Joyeeta G. Dastidar, Min Jiang
    Journal of Palliative Care.2018; 33(3): 167.     CrossRef
  • Predicting Hospital Readmission via Cost-Sensitive Deep Learning
    Haishuai Wang, Zhicheng Cui, Yixin Chen, Michael Avidan, Arbi Ben Abdallah, Alexander Kronzer
    IEEE/ACM Transactions on Computational Biology and Bioinformatics.2018; 15(6): 1968.     CrossRef
  • Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study
    Mohammad S. Alyahya, Heba H. Hijazi, Hussam A. Alshraideh, Amjad D. Al-Nasser
    Informatics for Health and Social Care.2017; 42(4): 361.     CrossRef
  • Identifying Potentially Avoidable Readmissions: A Medication‐Based 15‐Day Readmission Risk Stratification Algorithm
    Sreemanee Raaj Dorajoo, Vincent See, Chen Teng Chan, Joyce Zhenyin Tan, Doreen Su Yin Tan, Siti Maryam Binte Abdul Razak, Ting Ting Ong, Narendran Koomanan, Chun Wei Yap, Alexandre Chan
    Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy.2017; 37(3): 268.     CrossRef
  • The association between number of doctors per bed and readmission of elderly patients with pneumonia in South Korea
    Joo Eun Lee, Tae Hyun Kim, Kyoung Hee Cho, Kyu-Tae Han, Eun-Cheol Park
    BMC Health Services Research.2017;[Epub]     CrossRef
  • Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk
    Adam Steventon, John Billings
    BMJ Quality & Safety.2017; 26(10): 782.     CrossRef
  • Assessing risk of hospital readmissions for improving medical practice
    Parimal Kulkarni, L. Douglas Smith, Keith F. Woeltje
    Health Care Management Science.2016; 19(3): 291.     CrossRef
  • Comparison of Clinical Risk Factors Among Pediatric Patients With Single Admission, Multiple Admissions (Without Any 7-Day Readmissions), and 7-Day Readmission
    Jeffrey C. Winer, Elena Aragona, Alan I. Fields, David C. Stockwell
    Hospital Pediatrics.2016; 6(3): 119.     CrossRef
  • An ontology-based system to predict hospital readmission within 30 days
    Huda Al Ghamdi, Riyad Alshammari, Muhammad Imran Razzak
    International Journal of Healthcare Management.2016; 9(4): 236.     CrossRef
  • Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India
    Reena Duggal, Suren Shukla, Sarika Chandra, Balvinder Shukla, Sunil Kumar Khatri
    International Journal of Diabetes in Developing Countries.2016; 36(4): 469.     CrossRef
  • Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths
    Deepak Agrawal, Cheng-Bang Chen, Ronald W. Dravenstott, Christopher T. B. Strömblad, John Andrew Schmid, Jonathan D. Darer, Priyantha Devapriya, Soundar Kumara
    Medical Care.2016; 54(11): 1017.     CrossRef
  • Multimorbidity in risk stratification tools to predict negative outcomes in adult population
    Edurne Alonso-Morán, Roberto Nuño-Solinis, Graziano Onder, Giuseppe Tonnara
    European Journal of Internal Medicine.2015; 26(3): 182.     CrossRef
  • Predicting 30-day Hospital Readmission with Publicly Available Administrative Database
    K. Zhu, Z. Lou, J. Zhou, N. Ballester, N. Kong, P. Parikh
    Methods of Information in Medicine.2015; 54(06): 560.     CrossRef
  • Emergency Department Non-Urgent Visits and Hospital Readmissions Are Associated with Different Socio-Economic Variables in Italy
    Pamela Barbadoro, Elena Di Tondo, Vincenzo Giannicola Menditto, Lucia Pennacchietti, Februa Regnicoli, Francesco Di Stanislao, Marcello Mario D’Errico, Emilia Prospero, Chiara Lazzeri
    PLOS ONE.2015; 10(6): e0127823.     CrossRef
  • Using Decision Trees to Manage Hospital Readmission Risk for Acute Myocardial Infarction, Heart Failure, and Pneumonia
    John P. Hilbert, Scott Zasadil, Donna J. Keyser, Pamela B. Peele
    Applied Health Economics and Health Policy.2014; 12(6): 573.     CrossRef
  • A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study
    Samir E AbdelRahman, Mingyuan Zhang, Bruce E Bray, Kensaku Kawamoto
    BMC Medical Informatics and Decision Making.2014;[Epub]     CrossRef
  • Nationwide prospective study on readmission after umbilical or epigastric hernia repair
    F. Helgstrand, L. N. Jørgensen, J. Rosenberg, H. Kehlet, T. Bisgaard
    Hernia.2013; 17(4): 487.     CrossRef
  • Data Mining Application in Customer Relationship Management for Hospital Inpatients
    Eun Whan Lee
    Healthcare Informatics Research.2012; 18(3): 178.     CrossRef

JPMPH : Journal of Preventive Medicine and Public Health