[1] WU S，WU B，LIU M，et al. Stroke in China：

advances and challenges in epidemiology，prevention，

and management[J]. Lancet Neurol，2019，18（4）：

394-405.

[2] 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.

[3] 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.

[4] LI Z，JIANG Y，LI H，et al. China's response to the

rising stroke burden[J]. BMJ，2019，364：l879.

[5] PRABHAKARAN D，JHA D，PRIETO-MERINO

D，et al. Effectiveness of an mHealth-based

electronic decision support system for integrated

management of chronic conditions in primary care：

The mWellcare Cluster-Randomized Controlled

Trial[J/OL]. Circulation，2018[2020-01-10]. https：//

doi.org/10.1161/CIRCULATIONAHA.118.038192.

[6] CHANG J，RONCO C，ROSNER M H.

Computerized decision support systems：improving

patient safety in nephrology[J]. Nat Rev Nephrol，

2011，7（6）：348-355.

[7] LAMBIN P，van STIPHOUT R G，STARMANS M

H，et al. Predicting outcomes in radiation oncology-

-multifactorial decision support systems[J]. Nat Rev

Clin Oncol，2013，10（1）：27-40.

[8] CASTILLO R S，KELEMEN A. Considerations

for a successful clinical decision support system[J].

Comput Inform Nurs，2013，31（7）：319-326；quiz

327-328.

[9] AFZAL M，HUSSAIN M，ALI T，et al. Knowledgebased

query construction using the CDSS knowledge

base for efficient evidence retrieval[J]. Sensors

（Basel），2015，15（9）：21294-21314.

[10] Control conditions in mycin：a case study[DB/OL]

[2020-01-10]. https：//expertsystem101.weebly.com/

mycin.html.

[11] BRIGHT T J，WONG A，DHURJATI R，et al. Effect

of clinical decision-support systems：a systematic

review[J]. Ann Intern Med，2012，157（1）：29-43.

[12] RANTA A，DOVEY S，WEATHERALL M，et

al. Efficacy and safety of a TIA/stroke electronic

support tool（FASTEST）trial：study protocol[J].

Implement Sci，2012，7：107.

[13] LAVIN T L，RANTA A. Transient ischameic attack/

stroke electronic decision support：a 14-month safety

audit[J]. J Stroke Cerebrovasc Dis，2014，23（2）：

267-270.

[14] RANTA A，YANG C F，FUNNELL M，et al. Utility

of a primary care based transient ischaemic attack

electronic decision support tool：a prospective

sequential comparison[J]. BMC Fam Pract，2014，15：

86.

[15] 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.

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

Novel screening tool for stroke using artificial neural

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

[17] RODRIGUES G M，BARREIRA C，FROEHLER M，

et al. Multicenter ALADIN：automated large artery

occlusion detection in stroke imaging using artificial

intelligence[J/OL]. Stroke，2019，50（supple_1）：

Abstract WP71[2020-01-10]. https：//www.

ahajournals.org/doi/10.1161/str.50.suppl_1.WP71.

[18] STRAKA M，ALBERS G W，BAMMER R. Realtime

diffusion-perfusion mismatch analysis in acute

stroke[J]. J Magn Reson Imaging，2010，32（5）：

1024-1037.

[19] SHIEH Y，CHANG C H，SHIEH M，et al.

Computer-aided diagnosis of hyperacute stroke with

thrombolysis decision support using a contralateral

comparative method of CT image analysis[J]. J Digit

Imaging，2014，27（3）：392-406.

[20] NAGENTHIRAJA K，WALCOTT B P，HANSEN

M B，et al. Automated decision-support system for

prediction of treatment responders in acute ischemic

stroke[J]. Front Neurol，2013，4：140.

[21] FlYNN D，NESBITT D J，FORD G A，et al.

Development of a computerised decision aid for

thrombolysis in acute stroke care[J]. BMC Med

Inform Decis Mak，2015，15：6.

[22] ADAMS H P，BENDIXEN B H，KAPPELLE L

J，et al. Classification of subtype of acute ischemic

stroke. Definitions for use in a multicenter clinical

trial. TOAST. Trial of Org 10172 in Acute Stroke

Treatment[J]. Stroke，1993，24（1）：35-41.

[23] GOLDSTEIN L B，JONES M R，MATCHAR D B，

et al. Improving the reliability of stroke subgroup

classification using the Trial of Org 10172 in Acute

Stroke Treatment（TOAST）criteria[J]. Stroke，2001，

32（5）：1091-1098.

[24] MESCHIA J F，BARRETT K M，

CHUKWUDELUNZU F，et al. Interobserver

agreement in the trial of org 10172 in acute

stroke treatment classification of stroke based on

retrospective medical record review[J]. J Stroke

Cerebrovasc Dis，2006，15（6）：266-272.

[25] ARSAVA E M，BALLABIO E，BENNER T，et al.

The Causative Classification of Stroke system：an

international reliability and optimization study[J].

Neurology，2010，75（14）：1277-1284.

[26] AY H，BENNER T，MURAT ARSAVA E，et al. A

computerized algorithm for etiologic classification

of ischemic stroke[J]. Stroke，2007，38（11）：2979-

2984.

[27] NAM H S，CHA M J，KIM Y D，et al. Use of a handheld，computerized device as a decision support

tool for stroke classification[J]. Eur J Neurol，2012，

19（3）：426-430.

[28] 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.

[29] BACCHI S，OAKDEN-RAYNER L，ZERNER T，

et al. Deep Learning natural language processing

successfully predicts the cerebrovascular cause

of transient ischemic attack-like presentations[J].

Stroke，2019，50（3）：758-760.

[30] 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.

[31] ANDERSON J A，GODWIN K M，SALEEM J J，

et al. Accessibility，usability，and usefulness of a

web-based clinical decision support tool to enhance

provider-patient communication around Selfmanagement

TO Prevent（STOP）Stroke[J]. Health

Informatics J，2014，20（4）：261-274.

[32] ANDERSON J A，WILLSON P，PETERSON N J，

et al. Prototype to practice：developing and testing

a clinical decision support system for secondary

stroke prevention in a veterans healthcare facility[J].

Comput Inform Nurs，2010，28（6）：353-363.

[33] KARLSSON L O，NILSSON S，BANG 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.

[34] 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.

[35] BON HOMME M，REYNOLDS K K，VALDES

R Jr，et al. Dynamic pharmacogenetic models in

anticoagulation therapy[J]. Clin Lab Med，2008，28

（4）：539-552.

[36] CHASE D A，BARON S，ASH J S. Clinical decision

support and primary care acceptance of genomic

medicine[J]. Stud Health Technol Inform，2017，245：

700-703.