Feature Extraction and Visualization of Respiratory Therapist Notes for Pediatric Long-Term Ventilator Dependent Patients

Nathan M. Pajor*, Lindsay Nickels, Ezra Edgerton, Danny T.Y. Wu, Dan T. Benscoter, James J. Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Children with long-term ventilator dependence are a growing population that generate substantial cost to the healthcare system and require very lengthy admissions to initiate support. Respiratory therapy notes contain free-text descriptions of key respiratory events during these admissions but are underutilized. Using a retrospective electronic health record data set from 101 patients, we identified more clinically concerning patients, extracted key features from the free-text notes that differentiated these patients, and displayed those features in a timeline visualization that has implications for clinical decision support.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 Workshop on Visual Analytics in Healthcare, VAHC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-27
Number of pages2
ISBN (Electronic)9781665426442
DOIs
StatePublished - Nov 2020
Event2020 Workshop on Visual Analytics in Healthcare, VAHC 2020 - Virtual, Online, United States
Duration: Nov 14 2020Nov 18 2020

Publication series

NameProceedings - 2020 Workshop on Visual Analytics in Healthcare, VAHC 2020

Conference

Conference2020 Workshop on Visual Analytics in Healthcare, VAHC 2020
Country/TerritoryUnited States
CityVirtual, Online
Period11/14/2011/18/20

Keywords

  • information visualization
  • linguistic analysis
  • pediatrics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Health(social science)

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