Applying interpretable deep learning models to identify chronic cough patients using EHR data.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 1, 2021

Abstract

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction.

Authors

  • Xiao Luo
    Department of Spine Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Priyanka Gandhi
    Purdue School of Engineering and Technology, IUPUI, 799W Michigan St, Indianapolis, IN 46202, United States. Electronic address: prgandh@iu.edu.
  • Zuoyi Zhang
    Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States. Electronic address: zyizhang@indiana.edu.
  • Wei Shao
  • Zhi Han
    School of Microelectronics, Southeast University, Wuxi 214135, China. 220153639@seu.edu.cn.
  • Vasu Chandrasekaran
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: vasu.chandrasekaran@merck.com.
  • Vladimir Turzhitsky
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: vladimir.turzhitsky@merck.com.
  • Vishal Bali
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: vishal.bali@merck.com.
  • Anna R Roberts
    Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States. Electronic address: annarobe@regenstrief.org.
  • Megan Metzger
    Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States. Electronic address: mmw@iu.edu.
  • Jarod Baker
    Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States. Electronic address: bakerjar@regenstrief.org.
  • Carmen La Rosa
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: carmen.la.rosa@merck.com.
  • Jessica Weaver
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: jessica.weaver@merck.com.
  • Paul Dexter
    Regenstrief Institute Inc., Indianapolis, IN.
  • Kun Huang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. Kun.Huang@osumc.edu.

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