Real-World Data and Machine Learning to Predict Cardiac Amyloidosis
Autor:
García-García, Elena
; González-Romero, Gracia María
; Martín-Pérez, Encarna M.
; Zapata Cornejo, Enrique de Dios
; Escobar-Aguilar, Gema
; Cárdenas Bonnet, Marlon Félix
Fecha:
2021Palabra clave:
Revista / editorial:
International journal of environmental research and public healthTipo de Ítem:
Articulo Revista IndexadaDirección web:
https://www.mdpi.com/1660-4601/18/3/908Resumen:
(1) Background: Cardiac amyloidosis or “stiff heart syndrome” is a rare condition that
occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to
receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy
that is difficult to diagnose, often associated with a poor prognosis. This research analyses the
characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the
disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this
study are the basis of the learning and training techniques of the algorithm. The approach consisted of
using the information generated by the patients in each admission and discharge episode and treating
it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high
dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low
prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study,
the proposed approach demonstrates that it is possible to learn from clinical records despite the lack
of data. In the validation phase, the algorithm first acted on data from the general study population.
It then was applied to a sample of patients diagnosed with heart failure. The results revealed that
the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The
prediction levels showed that this technique could be useful in screening processes on a specific
population to detect the disease.
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