Applications of machine learning in familial hypercholesterolemia
Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1237258/full |
| _version_ | 1797672718369292288 |
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| author | Ren-Fei Luo Jing-Hui Wang Jing-Hui Wang Li-Juan Hu Qing-An Fu Si-Yi Zhang Long Jiang |
| author_facet | Ren-Fei Luo Jing-Hui Wang Jing-Hui Wang Li-Juan Hu Qing-An Fu Si-Yi Zhang Long Jiang |
| author_sort | Ren-Fei Luo |
| collection | DOAJ |
| description | Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH. |
| first_indexed | 2024-03-11T21:34:17Z |
| format | Article |
| id | doaj.art-dc025c99adf1446e942281b107c89150 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-03-11T21:34:17Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-dc025c99adf1446e942281b107c891502023-09-27T05:17:53ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-09-011010.3389/fcvm.2023.12372581237258Applications of machine learning in familial hypercholesterolemiaRen-Fei Luo0Jing-Hui Wang1Jing-Hui Wang2Li-Juan Hu3Qing-An Fu4Si-Yi Zhang5Long Jiang6Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Clinical Medicine, Nanchang University Queen Mary School, Nanchang, ChinaDepartment of Nursing, Nanchang Medical College, Nanchang, ChinaDepartment of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Clinical Medicine, Nanchang University Queen Mary School, Nanchang, ChinaDepartment of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, ChinaFamilial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.https://www.frontiersin.org/articles/10.3389/fcvm.2023.1237258/fullfamilial hypercholesterolemiamachine learningscreeningdiagnosisrisk assessment |
| spellingShingle | Ren-Fei Luo Jing-Hui Wang Jing-Hui Wang Li-Juan Hu Qing-An Fu Si-Yi Zhang Long Jiang Applications of machine learning in familial hypercholesterolemia Frontiers in Cardiovascular Medicine familial hypercholesterolemia machine learning screening diagnosis risk assessment |
| title | Applications of machine learning in familial hypercholesterolemia |
| title_full | Applications of machine learning in familial hypercholesterolemia |
| title_fullStr | Applications of machine learning in familial hypercholesterolemia |
| title_full_unstemmed | Applications of machine learning in familial hypercholesterolemia |
| title_short | Applications of machine learning in familial hypercholesterolemia |
| title_sort | applications of machine learning in familial hypercholesterolemia |
| topic | familial hypercholesterolemia machine learning screening diagnosis risk assessment |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1237258/full |
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