Browsing by Subject "Chronic cough"

Now showing 1 - 2 of 2
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    Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data
    (BMC, 2022-04-19) Shao, Wei; Luo, Xiao; Zhang, Zuoyi; Han, Zhi; Chandrasekaran, Vasu; Turzhitsky, Vladimir; Bali, Vishal; Roberts, Anna R.; Metzger, Megan; Baker, Jarod; La Rosa, Carmen; Weaver, Jessica; Dexter, Paul; Huang, Kun; Biostatistics and Health Data Science, School of Medicine
    Background: Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. Results: The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. Conclusions: To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.
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    Prescriptions of opioid-containing drugs in patients with chronic cough
    (Sage, 2024) Weiner, Michael; Liu, Ziyue; Schelfhout, Jonathan; Dexter, Paul; Roberts, Anna R.; Griffith, Ashley; Bali, Vishal; Weaver, Jessica; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. Objectives: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. Design: Retrospective cohort study. Methods: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. Results: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. Conclusion: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.