Development of patients triage algorithm from nationwide COVID-19
registry data based on machine learning
- URL: http://arxiv.org/abs/2109.09001v1
- Date: Sat, 18 Sep 2021 19:56:27 GMT
- Title: Development of patients triage algorithm from nationwide COVID-19
registry data based on machine learning
- Authors: Hyung Ju Hwang, Seyoung Jung, Min Sue Park, Hyeontae Jo
- Abstract summary: This paper provides the development processes of the severity assessment model using machine learning techniques.
Model only requires basic patients' basic personal data, allowing for them to judge their own severity.
We aim to establish a medical system that allows patients to check their own severity and informs them to visit the appropriate clinic center based on the past treatment details of other patients with similar severity.
- Score: 1.0323063834827415
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Prompt severity assessment model of confirmed patients who were infected with
infectious diseases could enable efficient diagnosis and alleviate the burden
on the medical system. This paper provides the development processes of the
severity assessment model using machine learning techniques and its application
on SARS-CoV-2 patients. Here, we highlight that our model only requires basic
patients' basic personal data, allowing for them to judge their own severity.
We selected the boosting-based decision tree model as a classifier and
interpreted mortality as a probability score after modeling. Specifically,
hyperparameters that determine the structure of the tree model were tuned using
the Bayesian optimization technique without any knowledge of medical
information. As a result, we measured model performance and identified the
variables affecting the severity through the model. Finally, we aim to
establish a medical system that allows patients to check their own severity and
informs them to visit the appropriate clinic center based on the past treatment
details of other patients with similar severity.
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