• Víctor Manuel Becerra-Muñoz, José Tomás Gómez Sáenz, and Escribano SubíasPilarPCentro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Hospital Universitario 12 de Octubre, Madrid, España..
• Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Servicio de Cardiología, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, España; Hospital Universitario Virgen de la Victoria, Universidad de Málaga (UMA), Málaga, España. Electronic address: vmbecerram@gmail.com.
• Med Clin (Barc). 2024 Jun 28; 162 (12): 591598591-598.

AbstractReal-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroupś definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.Copyright © 2024 Elsevier España, S.L.U. All rights reserved.

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