[1] Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden and mortality:estimates from monitoring, surveillance, and modelling[J]. Lancet Neurol, 2009, 8:345-354.
[2] Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, Wang L, Jiang Y, Li Y, Wang Y, Chen Z, Wu S, Zhang Y, Wang D, Wang Y, Feigin VL; NESS-China Investigators. Prevalence, incidence, and mortality of stroke in China:results from a nationwide population-based survey of 480687 adults[J]. Circulation, 2017, 135:759-771.
[3] Liu L, Wang D, Wong KS, Wang Y. Stroke and stroke care in China:huge burden, significant workload, and a national priority[J]. Stroke, 2011, 42:3651-3654.
[4] Reeves MJ, Mullard AJ, Wehner S. Inter-rater reliability of data elements from a prototype of the Paul Coverdell National Acute Stroke Registry[J]. BMC Neurol, 2008, 8:19.
[5] Abboud H, Labreuche J, Arauz A, Bryer A, Lavados PG, Massaro A, Munoz Collazos M, Steg PG, Yamout BI, Vicaut E, Amarenco P; OPTIC Registry Investigators. Demographics, socio-economic characteristics, and risk factor prevalence in patients with non-cardioembolic ischaemic stroke in low-and middle-income countries:the OPTIC registry[J]. Int J Stroke, 2013, 8 Suppl A100:4-13.
[6] Toschke AM, Wolfe CD, Heuschmann PU, Rudd AG, Gulliford M. Antihypertensive treatment after stroke and all-cause mortality:an analysis of the General Practitioner Research Database (GPRD)[J]. Cerebrovasc Dis, 2009, 28:105-111.
[7] Wang Y, Cui L, Ji X, Dong Q, Zeng J, Wang Y, Zhou Y, Zhao X, Wang C, Liu L, Nguyen-Huynh MN, Claiborne Johnston S, Wong L, Li H; China National Stroke Registry Investigators. The China National Stroke Registry for patients with acute cerebrovascular events:design, rationale, and baseline patient characteristics[J]. Int J Stroke, 2011, 6:355-361.
[8] Wang Y, Li Z, Zhao X, Wang D, Li H, Xian Y, Liu L, Wang Y. Stroke care quality in China:substantial improvement, and a huge challenge and opportunity[J]. Int J Stroke, 2017, 12:229-235.
[9] Li Z, Wang C, Zhao X, Liu L, Wang C, Li H, Shen H, Liang L, Bettger J, Yang Q, Wang D, Wang A, Pan Y, Jiang Y, Yang X, Zhang C, Fonarow GC, Schwamm LH, Hu B, Peterson ED, Xian Y, Wang Y, Wang Y; China National Stroke Registries. Substantial progress yet significant opportunity for improvement in stroke care in China[J]. Stroke, 2016, 47:2843-2849.
[10] Bronstein K, Murray P, Licata-Gehr E, Banko M, Kelly-Hayes M, Fast S, Kunitz S. The Stroke Data Bank project:implications for nursing research[J]. J Neurosci Nurs, 1986, 18:132-134.
[11] WHO MONICA Project Principal Investigators. The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease):a major international collaboration[J]. J Clin Epidemiol, 1988, 41:105-114.
[12] International Stroke Trial Collaborative Group. The International Stroke Trial (IST):a randomised trial of aspirin, subcutaneous heparin, both, or neither among 19435 patients with acute ischaemic stroke[J]. Lancet, 1997, 349:1569-1581.
[13] Hills NK, Johnston SC. Duration of hospital participation in a nationwide stroke registry is associated with improved quality of care[J]. BMC Neurol, 2006, 6:20.
[14] Anderson CS, Huang Y, Wang JG, Arima H, Neal B, Peng B, Heeley E, Skulina C, Parsons MW, Kim JS, Tao QL, Li YC, Jiang JD, Tai LW, Zhang JL, Xu E, Cheng Y, Heritier S, Morgenstern LB, Chalmers J; INTERACT Investigators. Intensive blood pressure reduction in acute cerebral haemorrhage trial (INTERACT):a randomised pilot trial[J]. Lancet Neurol, 2008, 7:391-399.
[15] Mayer SA, Brun NC, Begtrup K, Broderick J, Davis S, Diringer MN, Skolnick BE, Steiner T; FAST Trial Investigators. Efficacy and safety of recombinant activated factor Ⅶ for acute intracerebral hemorrhage[J]. N Engl J Med, 2008, 358:2127-2137.
[16] Wang Y, Jing J, Meng X, Pan Y, Wang Y, Zhao X, Lin J, Li W, Jiang Y, Li Z, Zhang X, Yang X, Ji R, Wang C, Wang Z, Han X, Wu S, Jia Z, Chen Y, Li H. The Third China National Stroke Registry (CNSR-Ⅲ) for patients with acute ischaemic stroke or transient ischaemic attack:design, rationale and baseline patient characteristics[J]. Stroke Vasc Neurol, 2019, 4:158-164.
[17] Wang Y, Li Z, Wang Y, Zhao X, Liu L, Yang X, Wang C, Gu H, Zhang F, Wang C, Xian Y, Wang DZ, Dong Q, Xu A, Zhao J. Chinese Stroke Center Alliance:a national effort to improve healthcare quality for acute stroke and transient ischaemic attack:rationale, design and preliminary findings[J]. Stroke Vasc Neurol, 2018, 3:256-262.
[18] Sun W, Ou Q, Zhang Z, Qu J, Huang Y. Chinese acute ischemic stroke treatment outcome registry (CASTOR):protocol for a prospective registry study on patterns of real-world treatment of acute ischemic stroke in China[J]. BMC Complement Altern Med, 2017, 17:357.
[19] Dong Y, Fang K, Wang X, Chen S, Liu X, Zhao Y, Guan Y, Cai D, Li G, Liu J, Liu J, Zhuang J, Wang P, Chen X, Shen H, Wang DZ, Xian Y, Feng W, Campbell BC, Parsons M, Dong Q. The network of Shanghai Stroke Service System (4S):a public health-care web-based database using automatic extraction of electronic medical records[J]. Int J Stroke, 2018, 13:539-544.
[20] Hsieh CY, Chen CH, Li CY, Lai ML. Validating the diagnosis of acute ischemic stroke in a National Health Insurance claims database[J]. J Formos Med Assoc, 2015, 114:254-259.
[21] Seghier ML, Patel E, Prejawa S, Ramsden S, Selmer A, Lim L, Browne R, Rae J, Haigh Z, Ezekiel D, Hope TMH, Leff AP, Price CJ. The PLORAS database:a data repository for predicting language outcome and recovery after stroke[J]. Neuroimage, 2016, 124(Pt B):1208-1212.
[22] Han JX, See AAQ, King NKK. Validation of prognostic models to predict early mortality in spontaneous intracerebral hemorrhage:a cross-sectional evaluation of a Singapore Stroke Database[J]. World Neurosurg, 2018, 109:e601-608.
[23] Schwamm L, Reeves MJ, Frankel M. Designing a sustainable national registry for stroke quality improvement[J]. Am J Prev Med, 2006, 31(6 Suppl 2):S251-257.
[24] Wang RZ, Chang JB, Feng M. Prospects for precious diagnosis, assessment, prediction and treatment of hemorrhagic stroke[J]. Zhongguo Xian Dai Shen Jing Ji Bing Za Zhi, 2019, 19:618-621.[王任直, 常健博, 冯铭. 出血性卒中精准诊断、评估、预测及治疗展望[J]. 中国现代神经疾病杂志, 2019, 19:618-621.]
[25] Renard F, Guedria S, Palma N, Vuillerme N. Variability and reproducibility in deep learning for medical image segmentation[J]. Sci Rep, 2020, 10:13724.
[26] Ding Y, Acosta R, Enguix V, Suffren S, Ortmann J, Luck D, Dolz J, Lodygensky GA. Using deep convolutional neural networks for neonatal brain image segmentation[J]. Front Neurosci, 2020, 14:207.
[27] Yang B, Chen W, Luo H, Tan Y, Liu M, Wang Y. Neuron image segmentation via learning deep features and enhancing weak neuronal structures[J]. IEEE J Biomed Health Inform, 2020.[Epub ahead of print]
[28] Kong Y, Kong X, He C, Liu C, Wang L, Su L, Gao J, Guo Q, Cheng R. Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning[J]. J Hematol Oncol, 2020, 13:88.
[29] Shin H, Oh S, Hong S, Kang M, Kang D, Ji YG, Choi BH, Kang KW, Jeong H, Park Y, Hong S, Kim HK, Choi Y. Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes[J]. ACS Nano, 2020, 14:5435-5444.
[30] Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome:a discovery and validation study[J]. Lancet, 2020, 395:350-360.
[31] Saha S, Pagnozzi A, Bourgeat P, George JM, Bradford D, Colditz PB, Boyd RN, Rose SE, Fripp J, Pannek K. Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model[J]. Neuroimage, 2020, 215:116807.
[32] Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research[J]. J Allergy Clin Immunol, 2020, 145:463-469.
[33] Lee C, Kim Y, Kim YS, Jang J. Automatic disease annotation from radiology reports using artificial intelligence implemented by a recurrent neural network[J]. AJR Am J Roentgenol, 2019, 212:734-740.
[34] Wakamiya S, Morita M, Kano Y, Ohkuma T, Aramaki E. Tweet classification toward twitter-based disease surveillance:new data, methods, and evaluations[J]. J Med Internet Res, 2019, 21:e12783.
[35] Chang JB, Jiang SZ, Chen XJ, Lok KH, Lee YL, Zhang QH, Wei JJ, Shi L, Feng M, Wang RZ. Consistency evaluation of an automatic segmentation for quantification of intracerebral hemorrhage using convolution neural network[J]. Zhongguo Xian Dai Shen Jing Ji Bing Za Zhi, 2020, 20:585-590.[常健博,姜燊种, 陈显金, 骆嘉希, 李沃霖, 张庆华, 魏俊吉, 石林, 冯铭, 王任直. 基于卷积神经网络的自发性脑出血血肿分割方法的一致性评价[J]. 中国现代神经疾病杂志, 2020, 20:585-590.]
[36] Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB, Roth K, Acosta J, Ruiz A, Phuah CL, Woo D, Gill TM, Sheth KN, Lee JM. Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage[J]. Stroke, 2020, 51:648-651.
[37] Zhao X, Chen K, Wu G, Zhang G, Zhou X, Lv C, Wu S, Chen Y, Xie G, Yao Z. Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema[J]. Eur Radiol, 2021.[Epub ahead of print]
[38] Stourac J, Dubrava J, Musil M, Horackova J, Damborsky J, Mazurenko S, Bednar D. FireProtDB:database of manually curated protein stability data[J]. Nucleic Acids Res, 2021, 49:D319-324.
[39] Zhou D, Tian F, Tian X, Sun L, Huang X, Zhao F, Zhou N, Chen Z, Zhang Q, Yang M, Yang Y, Guo X, Li Z, Liu J, Wang J, Wang J, Wang B, Zhang G, Sun B, Zhang W, Kong D, Chen K, Li X. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer[J]. Nat Commun, 2020, 11:2961.
[40] Misawa M, Kudo SE, Mori Y, Hotta K, Ohtsuka K, Matsuda T, Saito S, Kudo T, Baba T, Ishida F, Itoh H, Oda M, Mori K. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video)[J]. Gastrointest Endosc, 2020.[Epub ahead of print]
[41] Inaguma D, Kitagawa A, Yanagiya R, Koseki A, Iwamori T, Kudo M, Yuzawa Y. Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD:a machine learning-based prediction model by using a big database[J]. PLoS One, 2020, 15:e0239262.
[42] Stadler CB, Lindvall M, Lundström C, Bodén A, Lindman K, Rose J, Treanor D, Blomma J, Stacke K, Pinchaud N, Hedlund M, Landgren F, Woisetschläger M, Forsberg D. Proactive construction of an annotated imaging database for artificial intelligence training[J]. J Digit Imaging, 2021, 34:105-115.