Application of Machine Learning Based Approaches in Emergency Department to Support Clinical Decision Managing SARS-CoV-2 Infected Patients

• Conditions: Covid19

• Registration Number: NCT04825301
• Lead Sponsor: University of L'Aquila

Brief Summary

The aim of the study is to develop a prognostic prediction model based on machine learning algorithms in patients affected by coronavirus disease 2019 (COVID-19), the prediction model will be capable to recognize patient with favorable prognosis or patient with poor prognosis by intelligent systems data analysis.

Detailed Description

Not available

Study Timeline

• Study Start: 2020-02-27
• Study First Submitted: 2021-03-29
• Study First Submitted QC: 2021-03-29
• Study First Posted: 2021-04-01
• Status Verified: 2021-09
• Last Update Submitted: 2022-02-07
• Last Update Posted: 2022-02-24
• Primary Completion: 2022-03-30
• Study Completion: 2022-04-30

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 779

Inclusion Criteria

• patients aged over 18 positive for COVID-19 by polymerase chain reaction assay for rhino-pharyngeal swab

Exclusion Criteria

• Under 18 aged

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
COVID-19 clinical course	2 months	Data about sex, age, symptoms start date, symptoms, comorbidity, vital parameters, hematochemical blood tests, therapy, oxygen support, radiology, clinical disease progression will be collected. The collected data will be analyzed through a machine learning based approach to predict the prognosis of patients affected by COVID-19.

Secondary Outcome Measures

Name	Time	Method
Application of machine learning algorithms on data of patients affected by COVID-19	2 months	The collected data will be analyzed through a machine learning based approach to establish correlations between collected data and the prognosis of patients affected by COVID-19.

Trial Locations

Locations (1)

University of L'Aquila; 🇮🇹 L'Aquila, Italy