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Predictive Modeling of COVID and non-COVID Pneumonia Trajectories.
Mramorov, Nikita; Derevitskii, Ilya; Kovalchuk, Sergei.
  • Mramorov N; ITMO University, Saint Petersburg, Russia.
  • Derevitskii I; ITMO University, Saint Petersburg, Russia.
  • Kovalchuk S; ITMO University, Saint Petersburg, Russia.
Stud Health Technol Inform ; 285: 112-117, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1566635
ABSTRACT
Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2021 Document Type: Article Affiliation country: SHTI210582

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2021 Document Type: Article Affiliation country: SHTI210582