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Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB

Heart, Lung, Blood, and Sleep Disorders

Last data update: Feb 04, 2023. (Total: 52476 Documents since 2012)
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Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.
Yikuan Li et al. European heart journal. Digital health 2023 3(4) 535-547
Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
Jian Huang et al. Frontiers in public health 2023 101086339
Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis.
Xin Tong et al. BMC neurology 2023 23(1) 45
Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.
Artem Zatcepin et al. Zeitschrift fur medizinische Physik 2023
Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.
Chuan Hong et al. JAMA 2023 329(4) 306-317
Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence.
Ding Zhongao et al. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 2023
Imaging and Hemodynamic Characteristics of Vulnerable Carotid Plaques and Artificial Intelligence Applications in Plaque Classification and Segmentation.
Han Na et al. Brain sciences 2023 13(1)
Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population.
Orii Makoto et al. The international journal of cardiovascular imaging 2023
Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke.
Amann Julia et al. PloS one 2023 18(1) e0279088
Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.
Mansoori Amin et al. Scientific reports 2023 13(1) 663
Deep learning for collateral evaluation in ischemic stroke with imbalanced data.
Aktar Mumu et al. International journal of computer assisted radiology and surgery 2023
Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients.
Dagan Maya et al. Journal of clinical medicine 2023 12(1)
Systolic Blood Pressure and Cardiovascular Risk in Patients With Diabetes: A Prospective Cohort Study.
Rao Shishir et al. Hypertension (Dallas, Tex. : 1979) 2022
Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.
Zeng Ziqiang et al. PloS one 2022 17(12) e0279706
Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report.
Yeh Chih-Yang et al. Diagnostics (Basel, Switzerland) 2022 12(12)
Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
Sinski Maciej et al. Journal of clinical medicine 2022 11(24)
Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation.
Cui Shaoguo et al. Journal of personalized medicine 2022 12(12)
Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis.
Adamou Antonis et al. Journal of neurointerventional surgery 2022
Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning.
Feng Yuanchao et al. BMC medical research methodology 2022 22(1) 325
Prediction of incident cardiovascular events using machine learning and CMR radiomics.
Pujadas Esmeralda Ruiz et al. European radiology 2022
Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study.
Liu Jia et al. European radiology 2022
Deep transformation models for functional outcome prediction after acute ischemic stroke.
Herzog Lisa et al. Biometrical journal. Biometrische Zeitschrift 2022
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.
Raghunath Sushravya et al. Journal of electrocardiology 2022 7661-65
An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy.
Yao Zhelv et al. Journal of neurointerventional surgery 2022
Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.
Cui Liyuan et al. BioMed research international 2022 20222456550
An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.
Moghadam Saeed Montazeri et al. The Lancet. Digital health 2022 4(12) e884-e892
Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.
Fuadah Yunendah Nur et al. Diagnostics (Basel, Switzerland) 2022 12(11)
Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study.
Desai Antonio et al. International journal of environmental research and public health 2022 19(22)
A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.
Mavrogiorgou Argyro et al. Sensors (Basel, Switzerland) 2022 22(22)
The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy.
Shibata Soichiro et al. Neurology international 2022 14(4) 981-990
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