主动脉研究所的“大数据”分析掩盖了临床发现。

IF 2.5 3区 工程技术 Q2 BIOLOGY
Yale Journal of Biology and Medicine Pub Date : 2023-09-29 eCollection Date: 2023-09-01 DOI:10.59249/LNDZ2964
Mohammad A Zafar, Bulat A Ziganshin, Yupeng Li, Nicolai P Ostberg, John A Rizzo, Maryann Tranquilli, Sandip K Mukherjee, John A Elefteriades
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引用次数: 0

摘要

本期《耶鲁生物学与医学杂志》(YJBM)聚焦于医学研究中的大数据和精确分析。在耶鲁-纽黑文医院主动脉研究所,我们的绝大多数研究都来自我们的胸主动脉瘤(TAA)患者的大型前瞻性临床数据库,并辅以超大型基因测序文件。通过在这些临床和遗传数据库中应用先进的统计和人工智能技术,实现了以下基本临床和科学发现:来自传统“大数据”(大数据集)的分析。1.升主动脉瘤应在5cm处切除,以防止夹层和破裂。2.将主动脉大小与高度进行指数化可提高主动脉风险预测。3.主动脉根部扩张比升主动脉中段扩张更恶性。4.患有二叶主动脉瓣的升主动脉瘤患者的预后并不比先前假设的差。5.降主动脉和胸腹主动脉能够在不剥离的情况下破裂。6.女性TAA患者的表现比男性患者差。7.在预测主动脉夹层时,上行主动脉长度甚至优于主动脉直径。8.TAA疾病的“一线希望”是对动脉粥样硬化的终身保护。来自现代“大数据”机器学习/人工智能分析:1。TAA的机器学习模型:优于传统解剖标准。2.TAA基因检测及新致病基因的分离与发现。3.利用人工智能进行表型遗传表征。4.RNA小组“检测”TAA。这些发现基于(a)先进的传统统计分析在大型临床数据集中的长期应用,以及(b)耶鲁主动脉研究所最近在大型遗传数据集中应用先进的机器学习/人工智能,促进了TAA的诊断、医疗和外科治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

"Big Data" Analyses Underlie Clinical Discoveries at the Aortic Institute.

"Big Data" Analyses Underlie Clinical Discoveries at the Aortic Institute.

"Big Data" Analyses Underlie Clinical Discoveries at the Aortic Institute.

"Big Data" Analyses Underlie Clinical Discoveries at the Aortic Institute.

This issue of the Yale Journal of Biology and Medicine (YJBM) focuses on Big Data and precision analytics in medical research. At the Aortic Institute at Yale New Haven Hospital, the vast majority of our investigations have emanated from our large, prospective clinical database of patients with thoracic aortic aneurysm (TAA), supplemented by ultra-large genetic sequencing files. Among the fundamental clinical and scientific discoveries enabled by application of advanced statistical and artificial intelligence techniques on these clinical and genetic databases are the following: From analysis of Traditional "Big Data" (Large data sets). 1. Ascending aortic aneurysms should be resected at 5 cm to prevent dissection and rupture. 2. Indexing aortic size to height improves aortic risk prognostication. 3. Aortic root dilatation is more malignant than mid-ascending aortic dilatation. 4. Ascending aortic aneurysm patients with bicuspid aortic valves do not carry the poorer prognosis previously postulated. 5. The descending and thoracoabdominal aorta are capable of rupture without dissection. 6. Female patients with TAA do more poorly than male patients. 7. Ascending aortic length is even better than aortic diameter at predicting dissection. 8. A "silver lining" of TAA disease is the profound, lifelong protection from atherosclerosis. From Modern "Big Data" Machine Learning/Artificial Intelligence analysis: 1. Machine learning models for TAA: outperforming traditional anatomic criteria. 2. Genetic testing for TAA and dissection and discovery of novel causative genes. 3. Phenotypic genetic characterization by Artificial Intelligence. 4. Panel of RNAs "detects" TAA. Such findings, based on (a) long-standing application of advanced conventional statistical analysis to large clinical data sets, and (b) recent application of advanced machine learning/artificial intelligence to large genetic data sets at the Yale Aortic Institute have advanced the diagnosis and medical and surgical treatment of TAA.

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来源期刊
Yale Journal of Biology and Medicine
Yale Journal of Biology and Medicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.00
自引率
0.00%
发文量
41
期刊介绍: The Yale Journal of Biology and Medicine (YJBM) is a graduate and medical student-run, peer-reviewed, open-access journal dedicated to the publication of original research articles, scientific reviews, articles on medical history, personal perspectives on medicine, policy analyses, case reports, and symposia related to biomedical matters. YJBM is published quarterly and aims to publish articles of interest to both physicians and scientists. YJBM is and has been an internationally distributed journal with a long history of landmark articles. Our contributors feature a notable list of philosophers, statesmen, scientists, and physicians, including Ernst Cassirer, Harvey Cushing, Rene Dubos, Edward Kennedy, Donald Seldin, and Jack Strominger. Our Editorial Board consists of students and faculty members from Yale School of Medicine and Yale University Graduate School of Arts & Sciences. All manuscripts submitted to YJBM are first evaluated on the basis of scientific quality, originality, appropriateness, contribution to the field, and style. Suitable manuscripts are then subject to rigorous, fair, and rapid peer review.
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