Interactive visual analytics for medical data: application to COVID-19 clinical information during the first wave

  1. José M. Enguita 3
  2. Diego García 3
  3. María D. Chiara 12
  4. Nuria Valdés 24
  5. Ana González 3
  6. Abel A. Cuadrado 3
  7. Ignacio Díaz 3
  1. 1 Institute of Sanitary Research of the Principado de Asturias
  2. 2 Hospital Universitario Central de Asturias
    info

    Hospital Universitario Central de Asturias

    Oviedo, España

    ROR https://ror.org/03v85ar63

  3. 3 University of Oviedo, Dept of Electrical Engineering
  4. 4 Department of Internal Medicine, Section of Endocrinology and Nutrition
Erakutsi afiliazioak +
Aktak:
ESANN 2022 proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Argitalpen urtea: 2022

Biltzarra: European Symposium on Artificial Neural Networks, Computational Intelligence andMachine Learning (ESANN) (2022. Bruges, Belgium)

Mota: Biltzar ekarpena

DOI: 10.14428/ESANN/2022.ES2022-31 GOOGLE SCHOLAR

Laburpena

Biomedical data recorded as a result of clinical practiceare often multi-domain –involving lab measurements, medication, patientattributes, logistic information–, and also highly unstructured, with highrates of missing data and asynchronously sampled measurements. In thisscenario, we need tools capable of providing a broad picture prior to moredetailed analyses. We present here a visual analytics approach that usesthe morphing projections technique to combine the visualization of a t-SNEprojection of clinical time series, with views of other clinical or patient’sinformation. The proposed approach is demonstrated on an applicationcase study of COVID-19 clinical information taken during the first wave.

€ Ikusi finantzaketa

Finantzaketari buruzko informazioa

This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.

Finantzatzaile

  • Ministerio de Ciencia e Innovación Spain
    • PID2020-115401GB-I00