GSTCNet: gated spatio-temporal correlation network for stroke mortality prediction. (English) Zbl 1508.92117
References:
| [1] | Y, China stroke statistics 2019: A report from the national center for healthcare quality management in neurological diseases, China national clinical research center for neurological diseases, the Chinese stroke association, national center for chronic and non-communicable disease control and prevention, Chinese center for disease control and prevention and institute for global neuroscience and stroke collaborations, Stroke Vasc. Neurol., 5, 211-369 (2020) · doi:10.1136/svn-2020-000457 |
| [2] | S, Stroke in China: advances and challenges in epidemiology, prevention, and management, Lancet Neurol., 18, 394-405 (2019) · doi:10.1016/S1474-4422(18)30500-3 |
| [3] | T, Rapid transitions in the epidemiology of stroke and its risk factors in China from 2002 to 2013, Neurology, 89, 53-61 (2017) · doi:10.1212/WNL.0000000000004056 |
| [4] | P, Association of small dense low-density lipoprotein cholesterol with stroke risk, severity and prognosis, J. Atheroscler. Thromb., 27, 1310-1324 (2020) · doi:10.5551/jat.53132 |
| [5] | S, Association of general anesthesia vs procedural sedation with functional outcome among patients with acute ischemic stroke undergoing thrombectomy: a systematic review and meta-analysis, JAMA-J. Am. Med. Assoc., 322, 1283-1293 (2019) · doi:10.1001/jama.2019.11455 |
| [6] | C, Diabetes in ischaemic stroke in a regional Australian hospital - uncharted territory, Intern. Med. J., 52, 574-580 (2020) · doi:10.1111/imj.15073 |
| [7] | J, Acupuncture for post-stroke insomnia: A protocol for systematic review and meta-analysis, Medicine, 99, e21381 (2020) · doi:10.1097/MD.0000000000021381 |
| [8] | Y, Z. Yang. An improved self-supervised learning for EEG classification, Math. Biosci. Eng., 19, 6907-6922 (2022) · Zbl 1508.92128 · doi:10.3934/mbe.2022325 |
| [9] | R, A bag-of-words feature engineering approach for assessing health conditions using accelerometer data, Smart Health, 16, 100116 (2020) · doi:10.1016/j.smhl.2020.100116 |
| [10] | Z, Joint optimization of cycleGAN and CNN classifier for detection and localization of retinal pathologies on color fundus photographs, IEEE J. Biomed. Health, 26, 115-126 (2022) · doi:10.1109/JBHI.2021.3092339 |
| [11] | X, Machine learning for cataract classification/grading on ophthalmic imaging modalities: A survey, Mach. Intell. Res., 19, 184-208 (2022) · doi:10.1007/s11633-022-1329-0 |
| [12] | Y, Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke, Eur. Heart J., 41, ehaa946.0348 (2020) · doi:10.1093/ehjci/ehaa946.0348 |
| [13] | S, Multi-channel fusion LSTM for medical event prediction using EHRs, J. Biomed. Inf., 127, 104011 (2022) · doi:10.1016/j.jbi.2022.104011 |
| [14] | S, Interpretability analysis of one-Year mortality prediction for stroke patients based on deep neural network, IEEE J. Biomed. Health, 26, 1903-1910 (2022) · doi:10.1109/JBHI.2021.3123657 |
| [15] | S, Effect of prior stroke on stroke outcomes in patients with Ischemic cerebrovascular disease, Chin. Stroke J., 16, 1242-1247 (2021) · doi:10.3969/j.issn.1673-5765.2021.12.008 |
| [16] | E, Can we predict early recurrence in acute stroke?, Cerebrovasc. Dis., 18, 139-144 (2014) · doi:10.1159/000079267 |
| [17] | H, A score to predict early risk of recurrence after ischemic stroke, Neurology, 74, 128 (2010) · doi:10.1212/WNL.0b013e3181ca9cff |
| [18] | W, The stroke prognosis instrument ii (spi-ii): A clinical pre-diction instrument for patients with transient ischemia and nondisabling ischemic stroke, Stroke, 31, 456-462 (2000) · doi:10.1161/01.STR.31.2.456 |
| [19] | CAPRIE Steering Committee; A. randomised; blinded; trial of clopidogrel versus aspirin in patients at risk of ischaemic events (CAPRIE); Lancet; 348 (1996); 1329-1339, A randomised, blinded, trial of clopidogrel versus aspirin in patients at risk of ischaemic events (CAPRIE), Lancet, 348, 1329-1339 (1996) · doi:10.1016/s0140-6736(96)09457-3 |
| [20] | E, Prediction of early recurrence after acute ischemic stroke, JAMA Neurol., 73, 396-401 (2016) · doi:10.1001/jamaneurol.2015.4949 |
| [21] | L, A machine learning approach to predicting readmission or mortality in patients hospitalized for stroke or transient ischemic attack, Appl. Sci., 10, 6337 (2020) · doi:10.3390/app10186337 |
| [22] | A, Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study, Int. J. Cardiovas. Imag., 37, 1171-1187 (2021) · doi:10.1007/s10554-020-02099-7 |
| [23] | J, Machine learning based model for prediction of outcomes in acute stroke, Stroke, 50, 1263-1265 (2019) · doi:10.1161/STROKEAHA.118.024293 |
| [24] | C, A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation, Chin. Med. J. -Peking, 134, 6 (2021) · doi:10.1097/CM9.0000000000001515 |
| [25] | X, Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images, Med. Image Anal., 80, 102499 (2022) · doi:10.1016/j.media.2022.102499 |
| [26] | Y, TAHDNet: Time-aware hierarchical dependency network for medication recommendation, J. Biomed. Inf., 129, 104069 (2022) · doi:10.1016/j.jbi.2022.104069 |
| [27] | X, CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT, Knowl. -Based Syst., 250, 109109 (2022) · doi:10.1016/j.knosys.2022.109109 |
| [28] | S, Stroke lesion detection and analysis in MRI images based on deep learning, J. Healthc. Eng., 5, 1-9 (2021) · doi:10.1155/2021/5524769 |
| [29] | S, Interpretable CNN for ischemic stroke subtype classification with active model adaptation, BMC Med. Inf. Decis., 22, 3 (2022) · doi:10.1186/s12911-021-01721-5 |
| [30] | L, Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke, Eur. Radiol. Exp., 5, 25 (2021) · doi:10.1186/s41747-021-00225-1 |
| [31] | P. Chantamit-O-Pas, M. Goyal. Long short-term memory recurrent neural network for stroke prediction, in Proceeding of the Machine Learning and Data Mining in Pattern Recognition (MLDM), 2018,312-323.https://doi.org/10.1007/978-3-319-96136-1_25 |
| [32] | L, EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application, Front. Med. -PRC, 15 (2021) · doi:10.1007/s11684-020-0794-5 |
| [33] | Q, Prediction model of ischemic stroke recurrence using PSO-LSTM in mobile medical monitoring system, Comput. Intel. Neurosci., 2022, 8936103 (2022) · doi:10.1155/2022/8936103 |
| [34] | M, Integrating object proposal with attention networks for video saliency detection, Inf. Sci., 576, 819-830 (2021) · doi:10.1016/j.ins.2021.08.069 |
| [35] | M, Visual saliency detection by integrating spatial position prior of object with background cues, Exp. Syst. Appl., 168, 114219 (2021) · doi:10.1016/j.eswa.2020.114219 |
| [36] | T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in Proceeding of 2017 International Conference on Learning Representations (ICLR), 2017.https://doi.org/10.1145/3097983.3097997 |
| [37] | B, Label co-occurrence learning with graph convolutional networks for multi-label chest X-Ray image classification, IEEE J. Biomed. Health, 24, 2292-2302 (2020) · doi:10.1109/JBHI.2020.2967084 |
| [38] | E, An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation, J. Biomed. Inf., 125, 103968 (2022) · doi:10.1016/j.jbi.2021.103968 |
| [39] | W, Predicting drug response based on multi-omics fusion and graph convolution, IEEE J. Biomed. Health, 26, 1384-1393 (2022) · doi:10.1109/JBHI.2021.3102186 |
| [40] | C, MedGCN: Medication recommendation and lab test imputation via graph convolutional networks, J. Biomed. Inf., 127, 104000 (2022) · doi:10.1016/j.jbi.2022.104000 |
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