Unsupervised machine learning for the discovery of latent clusters in COVID-19 patients using electronic health records

Wanting Cui, Daniel Robins, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients' diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.

Original languageEnglish
Title of host publicationTHE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC
EditorsJohn Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias
PublisherIOS Press
Pages1-4
Number of pages4
ISBN (Electronic)9781643680927
DOIs
StatePublished - 2020

Publication series

NameStudies in Health Technology and Informatics
Volume272
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • Big Data Analytics
  • Unsupervised Machine Learning

Fingerprint

Dive into the research topics of 'Unsupervised machine learning for the discovery of latent clusters in COVID-19 patients using electronic health records'. Together they form a unique fingerprint.

Cite this