A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

Abstract Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develo...

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Príomhchruthaitheoirí:	Stephan Nopp, Clemens Spielvogel, Behnood Bikdeli, Ana Alberich-Conesa, Luis Hernández-Blasco, Mª Luisa Peris, Remedios Otero, David Jiménez, Manuel Monreal, Cihan Ay, The RIETE Investigators
Formáid:	Artigo
Teanga:	Inglês
Foilsithe / Cruthaithe:	Nature Portfolio 2025-09-01
Sraith:	Scientific Reports
Ábhair:	Pulmonary embolism Venous thromboembolism Machine learning Prediction Dyspnea Pulmonary arterial hypertension
Rochtain ar líne:	https://doi.org/10.1038/s41598-025-14893-1
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A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

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