Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine
暂无分享,去创建一个
Jie Wang | Wen Dan | Jinlei Liu | Qingyong He | Xiaoxue Zhong | Xudong Liu | Cheng Chen
[1] G. Thanassoulis,et al. Updated guidelines for the management of dyslipidemia and the prevention of cardiovascular disease in adults by pharmacists , 2023, Canadian pharmacists journal : CPJ = Revue des pharmaciens du Canada : RPC.
[2] Junjie Zhu,et al. Obesity and Dyslipidemia in Chinese Adults: A Cross-Sectional Study in Shanghai, China , 2022, Nutrients.
[3] Hager Saleh,et al. Predicting Breast Cancer Based on Optimized Deep Learning Approach , 2022, Computational intelligence and neuroscience.
[4] Jing Li,et al. Prevalence of Dyslipidemia and Availability of Lipid-Lowering Medications Among Primary Health Care Settings in China , 2021, JAMA network open.
[5] Jing Chen,et al. Study on the Effect of Macrophages on Vascular Endothelium in Mice With Different TCM Syndromes of Dyslipidemia and its Biological Basis Based on RNA-Seq Technology , 2021, Frontiers in Pharmacology.
[6] J. Aronson,et al. Associations between statins and adverse events in primary prevention of cardiovascular disease: systematic review with pairwise, network, and dose-response meta-analyses , 2021, BMJ.
[7] Florent Meyniel,et al. Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments , 2021, bioRxiv.
[8] Incheol Kim,et al. Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation , 2021, Sensors.
[9] G. Norata,et al. Global epidemiology of dyslipidaemias , 2021, Nature Reviews Cardiology.
[10] M. Banach,et al. Effects of statins on mitochondrial pathways , 2021, Journal of Cachexia, Sarcopenia and Muscle.
[11] Usha Ruby Dr.A,et al. Binary cross entropy with deep learning technique for Image classification , 2020 .
[12] Li Yang,et al. Study of cardiovascular disease prediction model based on random forest in eastern China , 2020, Scientific Reports.
[13] Li Yang,et al. Study of cardiovascular disease prediction model based on random forest in eastern China , 2020, Scientific Reports.
[14] Bin Huang,et al. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. , 2018, Annals of translational medicine.
[15] Abien Fred Agarap. Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.
[16] D. Matthews,et al. Prevalence, patterns, and associations of dyslipidemia among Sri Lankan adults-Sri Lanka Diabetes and Cardiovascular Study in 2005-2006. , 2018, Journal of clinical lipidology.
[17] Po-Yin Chang,et al. Triglyceride and HDL-C Dyslipidemia and Risks of Coronary Heart Disease and Ischemic Stroke by Glycemic Dysregulation Status: The Strong Heart Study , 2017, Diabetes Care.
[18] Jane C. Deng,et al. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model , 2016, PloS one.
[19] Anna Goldenberg,et al. TensorFlow: Biology's Gateway to Deep Learning? , 2016, Cell systems.
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Ming Guo,et al. Chinese Herbal Medicine on Dyslipidemia: Progress and Perspective , 2014, Evidence-based complementary and alternative medicine : eCAM.
[22] Francesco Pappalardo,et al. Mathematical modeling of biological systems , 2013, Briefings Bioinform..
[23] A. Tamakoshi,et al. Relation Between Serum Total Cholesterol Level and Cardiovascular Disease Stratified by Sex and Age Group: A Pooled Analysis of 65 594 Individuals From 10 Cohort Studies in Japan , 2012, Journal of the American Heart Association.
[24] J. Gallacher,et al. Lipid-related markers and cardiovascular disease prediction. , 2012, JAMA.
[25] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[26] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[27] Paulo J. G. Lisboa,et al. The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .
[28] Bogusław Stefaniak,et al. [Algorithms of artificial neural networks--practical application in medical science]. , 2005, Polski merkuriusz lekarski : organ Polskiego Towarzystwa Lekarskiego.
[29] S. Nicholls,et al. The emerging role of lipoproteins in atherogenesis: beyond LDL cholesterol. , 2004, Seminars in vascular medicine.
[30] L. Breiman. Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.
[31] W Penny,et al. Neural Networks in Clinical Medicine , 1996, Medical decision making : an international journal of the Society for Medical Decision Making.
[32] Claudio Moraga,et al. The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.
[33] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[34] D. Levy,et al. Cholesterol and mortality. 30 years of follow-up from the Framingham study. , 1987, JAMA.
[35] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[36] Shuangfeng Li,et al. Research Progress of Traditional Chinese Medicine in Treatment of Hyperlipidemia , 2013 .
[37] Mark D. Huffman,et al. Executive summary: heart disease and stroke statistics--2013 update: a report from the American Heart Association. , 2013, Circulation.
[38] Filippo Castiglione,et al. Mathematical and Computational Models in Tumor Immunology , 2012 .
[39] Thomas Lengauer,et al. Permutation importance: a corrected feature importance measure , 2010, Bioinform..
[40] T. Hastie,et al. Statistical Models in S , 1991 .