[1] GBD 2016 Stroke Collaborators. Global，regional，

and national burden of stroke，1990-2016：a

systematic analysis for the Global Burden of Disease

Study 2016[J]. Lancet Neurol，2019，18（5）：439-

458.

[2] WANG W，JIANG B，SUN H，et al. Prevalence，

incidence and mortality of stroke in China：results

from a nationwide population-based survey of 480，

687 adults[J]. Circulation，2017，135（8）：759-771.

[3] 张学高. 中国卫生健康统计年鉴2018[M]. 北京：中国

协和医科大学出版社，2018：1-409.

[4] LI Z，SINGHAL A B，WANG Y. Stroke physician

training in China[J/OL]. Stroke，2017，48（12）：

e338-e340[2020-01-10]. https：//doi.org/10.1161/

STROKEAHA.117.019462.

[5] LI Z，WANG C，ZHAO X，et al. Substantial

progress yet significant opportunity for improvement

in stroke care in China[J]. Stroke，2016，47（11）：

2843-2849.

[6] WANG Y L，LI Z X，ZHAO X Q，et al. Effect of

a multifaceted quality improvement intervention

on hospital personnel adherence to performance

measures in patients with acute ischemic stroke in

China：a randomized clinical trial[J]. JAMA，2018，

320（3）：245-254.

[7] WANG Y J，JING J，MENG X，et al. 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（3）：158-164.

[8] KUO F C，MAR B G，LINDSLEY R C，et al. The

relative utilities of genome-wide，gene panel，and

individual gene sequencing in clinical practice[J].

Blood，2017，130（4）：433-439.

[9] SHAMEER K，BADGELEY M A，MIOTTO R，et

al. Translational bioinformatics in the era of realtime

biomedical，health care and wellness data

streams[J]. Brief Bioinform，2017，18（1）：105-124.

[10] JAHAN R，SAVER J L，SCHWAMM L H，et

al. Association between time to treatment with

endovascular reperfusion therapy and outcomes in

patients with acute ischemic stroke treated in clinical

practice[J]. JAMA，2019，322（3）：252-263.

[11] GOYAL M，MENON B K，van ZWAM W H，et

al. Endovascular thrombectomy after large-vessel

ischaemic stroke：a meta-analysis of individual

patient data from five randomised trials[J]. Lancet，

2016，387（10029）：1723-1731.

[12] TITANO J J，BADGELEY M，SCHEFFLEIN J，et

al. Automated deep-neural-network surveillance of

cranial images for acute neurologic events[J]. Nat

Med，2018，24（9）：1337-1341.

[13] CHILAMKURTHY S，GHOSH R，TANAMALA

S，et al. Deep learning algorithms for detection of

critical findings in head CT scans：a retrospective

study[J]. Lancet，2018，392（10162）：2388-2396.

[14] CLAASSEN J，DOYLE K，MATORY A，et

al. Detection of brain activation in unresponsive

patients with acute brain injury[J]. N Engl J Med，

2019，380（26）：2497-2505.

[15] MURRAY N M，UNBERATH M，HAGER G D，et

al. Artificial intelligence to diagnose ischemic stroke

and identify large vessel occlusions：a systematic

review[J]. J Neurointerv Surg，2020，12（2）：156-

164.

[16] IBM. IBM Watson[DB/OL]. [2020-01-10]. https：//

www.ibm.com/watson.

[17] PIAZZA G，HURWITZ S，GALVIN C E，et al.

Alert-based computerized decision support for highrisk

hospitalized patients with atrial fibrillation

not prescribed anticoagulation：a randomized，

controlled trial（AF-ALERT）[J/OL]. Eur Heart J，

2019[2020-01-10]. https：//doi.org/10.1093/eurheartj/

ehz385.

[18] ABEDI V，GOYAL N，TSIVGOULIS G，et al.

Novel screening tool for stroke using artificial neural

network[J]. Stroke，2017，48（6）：1678-1681.

[19] WEIR C J，LEES K R，MACWALTER R S，et al.

Cluster-randomized，controlled trial of computerbased

decision support for selecting long-term antithrombotic

therapy after acute ischaemic stroke[J].

QJM，2003，96（2）：143-153.

[20] KARLSSON L O，NILSSON S，BÅNG M，et

al. A clinical decision support tool for improving

adherence to guidelines on anticoagulant therapy

in patients with atrial fibrillation at risk of stroke：a

cluster-randomized trial in a Swedish primary care

setting（the CDS-AF study）[J/OL]. PLoS Med，2018，

15（3）：e1002528[2020-01-10]. https：//doi.org/10.

1371/journal.pmed.1002528.

[21] HAN L，ASKARI M，ALTMAN R B，et al.

Atrial fibrillation burden signature and near-term

prediction of stroke：a machine learning analysis[J/

OL]. Circ Cardiovasc Qual Outcomes，2019，12

（10）：e005595[2020-01-10]. https：//doi.org/10.1161/

CIRCOUTCOMES.118.005595.

[22] SHARMA A M，GUPTA A，KUMAR P K，et al. A

review on carotid ultrasound atherosclerotic tissue

characterization and stroke risk stratification in

machine learning framework[J]. Curr Atheroscler Rep，2015，17（9）：55.

[23] HEO J，YOON J G，PARK H，et al. Machine

learning-based model for prediction of outcomes in

acute stroke[J]. Stroke，2019，50（5）：1263-1265.

[24] GARG R，OH E，NAIDECH A，et al. Automating

ischemic stroke subtype classification using machine

learning and natural language processing[J]. J Stroke

Cerebrovasc Dis，2019，28（7）：2045-2051.

[25] AL'AREF S J，MALIAKAL G，SINGH G，et

al. Machine learning of clinical variables and

coronary artery calcium scoring for the prediction

of obstructive coronary artery disease on coronary

computed tomography angiography：analysis from

the CONFIRM registry[J]. Eur Heart J，2020，41（3）：

359-367.

[26] LEE H，YUNE S，MANSOURI M，et al. An

explainable deep-learning algorithm for the detection

of acute intracranial haemorrhage from small

datasets[J]. Nat Biomed Eng，2019，3（3）：173-182.