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Predicting Hemodynamic Shock from Thermal Images using Machine Learning
Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and predic...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331545/ https://www.ncbi.nlm.nih.gov/pubmed/30643187 http://dx.doi.org/10.1038/s41598-018-36586-8 |