Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study [0.03%]
机器学习模型在预测急性冠脉综合征近期患者医疗费用中的应用：一项前瞻性试点研究
Arto J Hautala,Babooshka Shavazipour,Bekir Afsar et al.
Arto J Hautala et al.
Background: Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources. ...
Patient engagement with prescription refill text reminders across time and major societal events [0.03%]
跨时间及重大社会事件的患者用药提醒文本互动性研究
Joy Waughtal,Thomas J Glorioso,Lisa M Sandy et al.
Joy Waughtal et al.
Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction [0.03%]
可解释的SHAP-XGBoost模型在院内心肌梗死后死亡率中的应用
Constantine Tarabanis,Evangelos Kalampokis,Mahmoud Khalil et al.
Constantine Tarabanis et al.
Background: A lack of explainability in published machine learning (ML) models limits clinicians' understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. ...
Performance of alert transmissions from cardiac implantable electronic devices to the CareLink network: A retrospective analysis [0.03%]
植入式心脏电子设备向CareLink网络传输警报的性能：回顾性分析
Edmond M Cronin,Joseph C Green,Jeff Lande et al.
Edmond M Cronin et al.
Background: Remote monitoring of cardiac implantable electric devices improves patient outcomes and experiences. Alert-based systems notify physicians of clinical or device issues in near real-time, but their effectivenes...
The inaugural 2022 HRX meeting: A patient-centered digital health meeting for the acceleration of cardiovascular innovation [0.03%]
首届2022年HRX会议：以患者为中心的数字健康会议，旨在加速心血管创新
Sana M Al-Khatib,Jagmeet P Singh,Nassir Marrouche et al.
Sana M Al-Khatib et al.
Artificial intelligence-enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges [0.03%]
心血管医学中人工智能驱动工具的当前使用情况、认知和挑战的调查研究
Alexander Schepart,Arianna Burton,Larry Durkin et al.
Alexander Schepart et al.
Background: Numerous artificial intelligence (AI)-enabled tools for cardiovascular diseases have been published, with a high impact on public health. However, few have been adopted into, or have meaningfully affected, rou...
Association of cardiovascular health and risk prediction algorithms with subclinical atherosclerosis identified by carotid ultrasound [0.03%]
心血管健康与风险预测算法与颈动脉超声识别的亚临床动脉粥样硬化的相关性研究
Roberto Enrique Azcui Aparicio,Melinda J Carrington,Quan Huynh et al.
Roberto Enrique Azcui Aparicio et al.
Background: The requirement for laboratory tests to assess conventional cardiovascular disease (CVD) risk may be a barrier to the early detection and management of atherosclerosis in some population groups. A simpler risk...
Diagnostic accuracy of the PMcardio smartphone application for artificial intelligence-based interpretation of electrocardiograms in primary care (AMSTELHEART-1) [0.03%]
基于人工智能解释心电图的PMcardio智能手机应用程序在基层医疗中的诊断准确性（阿姆斯特心脏-1）
Jelle C L Himmelreich,Ralf E Harskamp
Jelle C L Himmelreich
Background: The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG. ...
Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence [0.03%]
使用人工智能评估24小时心电图记录中的房颤负担
Elisa Hennings,Michael Coslovsky,Rebecca E Paladini et al.
Elisa Hennings et al.
Background: Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-ba...
Artificial intelligence-enabled classification of hypertrophic heart diseases using electrocardiograms [0.03%]
基于心电图的机器学习技术在肥厚性心脏病中的分类应用
Julian S Haimovich,Nate Diamant,Shaan Khurshid et al.
Julian S Haimovich et al.
Background: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. Objective: ...