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Updated: Jun 6, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Published on: May 19, 2023

Machine learning predicts pulmonary Long Covid sequelae using clinical data.

Ermanno Cordelli¹, Paolo Soda^2,3, Sara Citter^4,5

¹Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome, 00128, Italy.

BMC Medical Informatics and Decision Making

|November 28, 2024

Summary

This summary is machine-generated.

Machine learning models can predict pulmonary Long COVID complications using hospitalization data. Early prediction of Long COVID sequelae is crucial for timely intervention and improved patient outcomes.

Keywords:

Artificial intelligence Long-COVID Multimodal learning Post-COVID syndrome Prognosis

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

Medical Informatics
Pulmonology
Machine Learning

Background:

Long COVID is a multi-systemic condition impacting quality of life, frequently involving pulmonary complications.
Early prediction of Long COVID sequelae is essential for timely intervention and preventing severe outcomes.

Purpose of the Study:

To investigate machine learning approaches for predicting pulmonary Long COVID sequelae using clinical hospitalization data.
To develop predictive models for early identification of patients at risk for Long COVID pulmonary complications.

Main Methods:

Utilized three distinct machine learning approaches: a traditional shallow learner, an ensemble of classifiers, and a multimodality-driven model.
Trained and evaluated models on clinical data from 152 patients hospitalized with COVID-19.

Main Results:

Achieved predictive accuracy of up to for pulmonary Long COVID sequelae.
Demonstrated the feasibility of using machine learning on clinical data for early prediction.

Conclusions:

Machine learning models show significant potential in predicting Long COVID pulmonary complications.
The study contributes a publicly available dataset to advance research in Long COVID prediction.