ArticleBMC medical informatics and decision making2024
Machine learning predicts pulmonary Long Covid sequelae using clinical data.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identifying risk factors of post-COVID-19 conditions with machine learning and deep learning algorithms.Global epidemiology · 2025Article
- Decoding long COVID-associated cardiovascular dysfunction: Mechanisms, models, and new approach methodologies.Journal of molecular and cellular cardiology · 2025Review
- Longitudinal wearable sensor data enhance precision of Long COVID detection.PLOS digital health · 2025Article
- Immunomodulatory Mechanisms Underlying Neurological Manifestations in Long COVID: Implications for Immune-Mediated Neurodegeneration.International journal of molecular sciences · 2025Review
- Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment. In this work, we investigated three machine learning approaches that use clinical data collected at the time of hospitalization to this goal. The first works with all the descriptors feeding a traditional shallow learner, the second exploits the benefits of an ensemble of classifiers, and the third is driven by the intrinsic multimodality of the data so that different models learn complementary information. The experiments on a new cohort of data from 152 patients show that it is possible to predict pulmonary Long Covid sequelae with an accuracy of up to
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Registered trials
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