Using Artificial Intelligence to Better Predict and Develop Biomarkers.
| Autor: | Michelhaugh SA; Georgetown University School of Medicine, Washington, DC, USA., Januzzi JL Jr; Department of Medicine, Division of Cardiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA; Department of Medicine, Division of Cardiology, Harvard Medical School, Boston, MA, USA; Baim Institute for Clinical Research, Boston, MA, USA. Electronic address: jjanuzzi@partners.org. |
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| Jazyk: | angličtina |
| Zdroj: | Heart failure clinics [Heart Fail Clin] 2022 Apr; Vol. 18 (2), pp. 275-285. Date of Electronic Publication: 2022 Mar 08. |
| DOI: | 10.1016/j.hfc.2021.11.004 |
| Abstrakt: | Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care. Competing Interests: Disclosure Dr J.L. Januzzi is supported in part by the Hutter Family Professorship; has been a trustee of the American College of Cardiology; has received grant support from Novartis Pharmaceuticals and Abbott Diagnostics; has received consulting income from Abbott, Janssen, Novartis, and Roche Diagnostics; has participated in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, CVRx, Janssen, MyoKardia, and Takeda. Mr S.A. Michelhaugh has no relationships to disclose. (Copyright © 2021 Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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