Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.
Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.
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DOI：
10.1016/j.hfc.2021.12.002
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发表时间：
2022-04
影响因子：
3.4
通讯作者：
Shah SJ
中科院分区：
文献类型：
--
作者：
Ahmad FS;Luo Y;Wehbe RM;Thomas JD;Shah SJ
射血分数保留的心力衰竭（HF）（HFpEF）代表了一种典型的心血管疾病，其中机器学习可以改善靶向治疗和对发病机制的机械理解。机器学习涉及从数据中学习的算法，有可能指导HFpEF等复杂临床综合征的精准医学方法。因此，重要的是要了解机器学习的潜在效用和常见陷阱，以便能够适当地应用和解释它。可受益于机器学习的HFpEF未满足的需求包括：（1）HFpEF及其病因的早期诊断;（2）改善HF的分类（亚表型）;（3）通过预测治疗反应性和HF住院治疗改善HFpEF的管理;（4）改善临床试验中HFpEF患者的识别。机器学习主要有两种设置，监督学习和无监督学习。在监督学习中，预定义的结果或标签用于开发模型，以帮助预测未来的结果。在无监督学习中，算法应用于未标记的数据，以了解数据中的内在模式（例如，用于HF亚型的新分类）。这些技术可以应用于许多不同的数据类型，包括临床特征、心血管诊断测试（即成像、实验室、心电图、传感器）和临床记录。虽然机器学习为HFpEF带来了相当大的希望，但它受到几个潜在陷阱的影响（例如，偏见，混淆，过度拟合和缺乏外部验证），这些都是解释机器学习研究时需要考虑的重要因素。
Heart failure (HF) with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Unmet needs in HFpEF that could benefit from machine learning include:(1) earlier diagnosis of HFpEF and its etiology;(2) improved classification (sub-phenotyping) of HF;(3) improved management of HFpEF through prediction of therapeutic responsiveness and HF hospitalization; and (4) improved identification of HFpEF patients for clinical trials. There are two main settings of machine learning, supervised learning and unsupervised learning. In supervised learning, predefined outcomes or labels are used to develop models to help predict future outcomes. In unsupervised learning, algorithms are applied to unlabeled data to understand intrinsic patterns within the data (eg, for novel classification of HF subtypes). These techniques can be applied to many different data types, including clinical characteristics, cardiovascular diagnostic testing (ie, imaging, labs, electrocardiograms, sensors), and clinical notes. While machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls (eg, bias, confounding, overfitting, and lack of external validation), which are important factors to consider when interpreting machine learning studies.
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影响因子：
16.6
作者：
Goto S;Mahara K;Beussink-Nelson L;Ikura H;Katsumata Y;Endo J;Gaggin HK;Shah SJ;Itabashi Y;MacRae CA;Deo RC
通讯作者：
Deo RC
影响因子：
4.9
作者：
Byrd RJ;Steinhubl SR;Sun J;Ebadollahi S;Stewart WF
通讯作者：
Stewart WF
影响因子：
2.9
作者：
Collins GS;Dhiman P;Andaur Navarro CL;Ma J;Hooft L;Reitsma JB;Logullo P;Beam AL;Peng L;Van Calster B;van Smeden M;Riley RD;Moons KG
通讯作者：
Moons KG
影响因子：
18.2
作者：
Adler, Eric D.;Voors, Adriaan A.;Yagil, Avi
通讯作者：
Yagil, Avi
影响因子：
18.2
作者：
Greenberg, Barry;Adler, Eric;Yagil, Avi
通讯作者：
Yagil, Avi