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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Author

Listed:
  • Shu-Farn Tey

    (Pulmonary Medicine, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Chung-Feng Liu

    (Department of Medical Research, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Tsair-Wei Chien

    (Department of Medical Research, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Chin-Wei Hsu

    (Department of Pharmacy, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Kun-Chen Chan

    (Division of Clinical Pathology, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Chia-Jung Chen

    (Department of Information Systems, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Tain-Junn Cheng

    (Departments of Neurology and Occupational Medicine, Chi-Mei Medical Center, Tainan 700, Taiwan)

  • Wen-Shiann Wu

    (Division of Cardiovascular Medicine, Chi-Mei Medical Center, Tainan 700, Taiwan
    Department of Pharmacy, Chia-Nan University of Pharmacy and Science, Tainan 700, Taiwan)

Abstract

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training ( n = 15,324; ≅70%) and test ( n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Suggested Citation

  • Shu-Farn Tey & Chung-Feng Liu & Tsair-Wei Chien & Chin-Wei Hsu & Kun-Chen Chan & Chia-Jung Chen & Tain-Junn Cheng & Wen-Shiann Wu, 2021. "Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)," IJERPH, MDPI, vol. 18(10), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5110-:d:552788
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    References listed on IDEAS

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    1. Ben O L Mellors & Abigail M Spear & Christopher R Howle & Kelly Curtis & Sara Macildowie & Hamid Dehghani, 2020. "Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    2. N. Verhelst & I.W. Molenaar, 1988. "Logit based parameter estimation in the Rasch model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 42(4), pages 273-295, December.
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