Real-World Data and Machine Learning to Predict Cardiac Amyloidosis
| Autor: | Gracia María González-Romero, Encarna M Martín-Pérez, Gema Escobar-Aguilar, Elena García-García, Enrique de Dios Zapata Cornejo, Marlon Félix Cárdenas Bonnet |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Heart Diseases Health Toxicology and Mutagenesis lcsh:Medicine Disease 030204 cardiovascular system & hematology Article Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans 030212 general & internal medicine Intensive care medicine Heart Failure Stiff heart syndrome business.industry Amyloidosis Myocardium lcsh:R Public Health Environmental and Occupational Health Restrictive cardiomyopathy cardiac amyloidosis medicine.disease artificial intelligence predictive models real-world data (RWD) Cardiac amyloidosis Heart failure cardiovascular system Population study business Real world data |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 18 Issue 3 International Journal of Environmental Research and Public Health, Vol 18, Iss 908, p 908 (2021) |
| ISSN: | 1660-4601 |
| Popis: | (1) Background: Cardiac amyloidosis or &ldquo stiff heart syndrome&rdquo 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. |
| Databáze: | OpenAIRE |
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