Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters
Research on Biomedical Engineering
; 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20236113
ABSTRACT
Purpose:
In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease.Objective:
In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice.Method:
We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil.Results:
We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%.Conclusion:
By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.
Clinical diagnosis support; Computer-aided diagnosis; Covid-19; Covid-19 rapid protocols; Hematological parameters; Software-based rapid test; Artificial intelligence; Computer aided diagnosis; Decision support systems; Decision trees; Internet protocols; Particle swarm optimization (PSO); Software testing; Clinical diagnose support; Clinical diagnosis; Clinical practices; Covid-19 rapid protocol; Diagnosis support; Rapid test; Sensitivity and specificity; Intensive care units
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Tópicos:
Vacunas
Idioma:
Inglés
Revista:
Research on Biomedical Engineering
Año:
2023
Tipo del documento:
Artículo
Similares
MEDLINE
...
LILACS
LIS