Evidence map›Paper›PMID 35439945›Full record

ArticleBMC bioinformatics2022

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data.

Wei Shao, Xiao Luo, Zuoyi Zhang, Zhi Han, Vasu Chandrasekaran, Vladimir Turzhitsky, Vishal Bali, Anna R Roberts, Megan Metzger, Jarod Baker and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Wei ShaoIndiana University School of Medicine, 1101 W 10th Street, Indianapolis, IN, 46202, USA.
Xiao LuoPurdue School of Engineering and Technology, IUPUI, ET 301L, 799 W. Michigan Street, Indianapolis, IN, 46202, USA. luo25@iupui.edu.ORCID http://orcid.org/0000-0002-3649-9785
Zuoyi ZhangIndiana University School of Medicine, 1101 W 10th Street, Indianapolis, IN, 46202, USA.
Zhi HanIndiana University School of Medicine, 1101 W 10th Street, Indianapolis, IN, 46202, USA.
Vasu ChandrasekaranCenter for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ, USA.
Vladimir TurzhitskyCenter for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ, USA.
Vishal BaliCenter for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ, USA.
Anna R RobertsRegenstrief Institute, Inc., Indianapolis, IN, USA.
Megan MetzgerRegenstrief Institute, Inc., Indianapolis, IN, USA.
Jarod BakerRegenstrief Institute, Inc., Indianapolis, IN, USA.
Carmen La RosaCenter for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ, USA.
Jessica WeaverCenter for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ, USA.
Paul DexterIndiana University School of Medicine, 1101 W 10th Street, Indianapolis, IN, 46202, USA.
Kun HuangIndiana University School of Medicine, 1101 W 10th Street, Indianapolis, IN, 46202, USA. kunhuang@iu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic 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.

resultsThe 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.

conclusionsTo 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.

Indexed as

Deep LearningAdultAlgorithmsCluster AnalysisCoughHumansChronic coughDeep clusteringEMR dataUnsupervised learning

Identifiers

PMID35439945
PMCPMC9019947

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.