This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Enabling Longitudinal Exploratory Analysis of Clinical COVID Data (preprint)
arxiv; 2021.
Preprint
in English
| PREPRINT-ARXIV | ID: ppzbmed-2108.11476v1
ABSTRACT
As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-ARXIV
Main subject:
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS