人工智能在阿尔茨海默病临床诊疗中的研究热点及前沿趋势分析

doi:10.12114/j.issn.1007-9572.2023.0704

中国全科医学 ›› 2024, Vol. 27 ›› Issue (26): 3218-3226.DOI: 10.12114/j.issn.1007-9572.2023.0704

人工智能在阿尔茨海默病临床诊疗中的研究热点及前沿趋势分析

余如霞¹, 姜婧¹^,^*(), 王湫澄¹, 王越¹, 赵小月²

1.100029　北京市，北京中医药大学护理学院
2.100029　北京市，北京中医药大学针灸推拿学院

收稿日期:2023-10-10 修回日期:2024-01-10 出版日期:2024-09-15 发布日期:2024-06-14
通讯作者: 姜婧
作者贡献：
余如霞、姜婧提出研究设想及总体研究方案的构建，负责论文的撰写与修改并对文章负责；王湫澄和王越收集、筛选和整理研究数据，以供研究使用和结果重现；赵小月对图片格式及文字修订，整理参考文献。
基金资助:
国家自然科学基金面上项目(82174515); 2023年北京中医药大学研究生自主科研课题(ZJKT2023070)

The Hotspots and Frontier Trends of Artificial Intelligence in the Clinical Diagnosis and Treatment of Alzheimer's Disease: Bibliometric Analysis of the Past 20 Years

YU Ruxia¹, JIANG Jing¹^,^*(), WANG Qiucheng¹, WANG Yue¹, ZHAO Xiaoyue²

1.School of Nursing, Beijing University of Traditional Chinese Medicine, Beijing 100029, China
2.School of Acupuncture-moxibustion and Tuina, Beijing University of Traditional Chinese Medicine, Beijing 100029, China

Received:2023-10-10 Revised:2024-01-10 Published:2024-09-15 Online:2024-06-14
Contact: JIANG Jing

摘要/Abstract

摘要： 背景目前，人工智能应用于阿尔茨海默病（AD）领域的研究论文数量增幅较大，明确该领域最新研究热点和未来发展趋势十分重要。目的通过应用文献计量学分析，总结人工智能应用于AD领域的相关研究，阐明2004—2023年的研究热点和未来研究趋势。方法在Web of Science核心数据库中检索了2004年1月—2023年6月关于人工智能应用于AD领域的文献。采用Microsoft Office Excel、CiteSpace和VOSviewer软件对发文量、国家、作者、机构、关键词和共引网络进行可视化分析。结果最终纳入了3 189篇文献。自2004年以来有关人工智能应用于AD领域的文献数量稳步增加，并从2015年起进入快速增长阶段，最高突破600篇。共有94个国家、3 930家机构、13 563位作者以及52 019位被引作者参与了此研究。其中，美国和中国在这一领域中处于领先地位；韩国大学发文量位列第一；此外，ZHANG DAOQIANG、LIU MINGXIA、SUK HEUNG-IL和CLIFFFORD R. JACK Jr不仅是高产的作者还是被引次数最多的作者。根据关键词和文献被引结果的可视化分析，发现AD的诊断与病程分类、预测AD的风险因素是当前的研究热点，任务分析是未来人工智能应用于AD领域中的研究趋势。结论人工智能应用于AD领域已经引起了全球研究者的广泛关注，AD的诊断与病程分类、预测AD的风险因素是当前的研究热点，而任务分析中的辅助药物的研发、个性化治疗和护理以及提升人工智能的算法性能可能会成为未来的研究趋势。

关键词: 阿尔茨海默病, 痴呆, 人工智能, CiteSpace, VOSviewer, 文献计量学

Abstract:

Background

Currently, the number of research papers on the application of artificial intelligence to the field of Alzheimer's disease (AD) has increased significantly. It is important to clarify the latest research hotspots and future development trends in this field.

Objective

To summarize the relevant research on the application of artificial intelligence to AD through bibliometric analysis, and clarify the research hotspots and trends from 2004 to 2023.

Methods

Literature on the application of artificial intelligence to AD from January 2004 to June 2023 was searched for in the Web of Science core database, and Microsoft Office Excel, CiteSpace, and VOSviewer software were used to visually analyze the number of publications, countries, authors, institutions, keywords, and co-citation networks of the literature.

Results

Ultimately 3 189 articles were included. The number of literature on the application of artificial intelligence to AD has steadily increased since 2004 and has grown rapidly since 2015, with a maximum of over 600 articles. A total of 94 countries, 3 930 institutions, 13 563 authors, and 52 019 cited authors participated in this study. Among them, the United States and China were in a leading position in this field; Republic of Korea universities ranked first in terms of the number of publications; In addition, ZHANG DAOQIANG, LIU MINGXIA, SUK HEUNG-IL, and CLIFFORD R. JACK Jr were not only prolific authors but also the authors with the most citations. The visualization analysis of keywords and literature citations revealed that regarding the application of artificial intelligence to AD, the diagnosis and disease course classification of AD, as well as the prediction of its risk factors, are current research hotspots and that task analysis are future research trends.

Conclusion

The application of artificial intelligence to AD has attracted widespread attention from researchers worldwide. The diagnosis and classification of AD, as well as the prediction of its risk factors, are current research hotspots. Developing adjunctive drugs in task analysis, personalized treatment and care, and improving the algorithm performance of artificial intelligence may be research trends in the future.

Key words: Alzheimer disease, Dementia, Artificial intelligence, CiteSpace, VOSviewer, Bibliometrics

中图分类号:

R 745.7

余如霞,姜婧,王湫澄,等. 人工智能在阿尔茨海默病临床诊疗中的研究热点及前沿趋势分析[J]. 中国全科医学, 2024, 27(26): 3218-3226. DOI: 10.12114/j.issn.1007-9572.2023.0704.
YU Ruxia,JIANG Jing,WANG Qiucheng, et al. The Hotspots and Frontier Trends of Artificial Intelligence in the Clinical Diagnosis and Treatment of Alzheimer's Disease: Bibliometric Analysis of the Past 20 Years[J]. Chinese General Practice, 2024, 27(26): 3218-3226. DOI: 10.12114/j.issn.1007-9572.2023.0704.

图/表 12

图1 人工智能应用于AD领域的文献检索策略

Figure 1 Literature retrieval strategy for artificial intelligence application in the field of AD

表1 2004—2023年人工智能应用于AD领域的检索词

Table 1 Search terms for artificial intelligence applications in the field of AD from 2004 to 2023

步骤	检索词
1	dementia；Alzheimer Disease；Alzheimer's Disease
2	artificial intelligence；machine intelligence；computational intelligence；intelligent learning；machine learning；deep learning；neural learning；feature^* learning；supervised learning；neural network^；unsupervised clustering；feature^ mining；data mining；deep network^；image^ segmentation；graph mining；feature^* selection；data clustering；semantic segmentation；knowledge graph；feature^* extraction；big data；expert^* system^；bayes^ network；neural nets model
3	1 AND 2

图2 2004—2023年人工智能应用于AD领域的年度发文量

Figure 2 Annual number of published papers on the application of artificial intelligence in AD research from 2004 to 2023

图3 机构合作网络图谱注：节点大小表示机构的发文数量，节点越大发文量越多；线条的粗细代表各个机构之间的合作强度，线条越粗合作越强。

Figure 3 Institutional cooperation network graph

图4 作者合作网络图谱注：节点大小表示作者的发文数量，节点越大发文量越多；不同线条的颜色代表不同的集群；线条的粗代表作者之间的合作强度，线条越粗合作越强。

Figure 4 Author collaboration network graph

表2 发文量排名前10的作者

Table 2 Top 10 authors in terms of publication volume

顺位	作者	发文量（篇）
1	SHEN DINGGANG	72
2	ZHANG DAOQIANG	32
3	LIU MINGXIA	30
4	THOMPSON PAUL M	21
5	SUK HEUNG-IL	20
6	WANG LEI	19
7	LEE KUN HO	18
8	WESTMAN ERIC	17
9	HAMPEL HARALD	16
10	CLIFFFORD R. JACK Jr	16

表3 被引频次排名前10的作者

Table 3 Top 10 authors ranked in co-citation frequency

顺位	共被引作者	被引频次（次）
1	CLIFFFORD R. JACK Jr	1 388
2	PETERSEN RONALD C	808
3	ZHANG DAOQIANG	600
4	SUK HEUNG-IL	535
5	FISCHL BRUCE	510
6	ASHBURNER JOHN	484
7	BRAAK HEIKO	409
8	LIU MANHUA	406
9	LIU MINGXIA	403
10	DUBOIS BRUNO	398

图5 排名前10的被引文献网络分析注：节点的大小代表被引次数，节点越大则被引次数越多；节点间的连线表示二者存在共被引关系。

Figure 5 Network analysis of top 10 co-cited literature

图6 2004—2014年关键词共现图谱注：圆圈表示关键词，圆圈的面积越大则关键词出现的频次越高；紫色外圈越粗则中心度越高；圆圈之间的线条越粗表示合作越密切。

Figure 6 Keyword co-occurrence graph from 2004 to 2014

图7 2015—2023年关键词共现图谱注：圆圈表示关键词，圆圈的面积越大则关键词出现的频次越高；紫色外圈越粗则中心度越高；圆圈之间的线条越粗表示合作越密切。

Figure 7 Keyword co-occurrence graph from 2015 to 2023

表4 2015—2023年热点关键词

Table 4 Hot keywords from 2015 to 2023

类别	名称	频次（次）
模型	machine learning（机器学习）	460
	deep learning（深度学习）	322
	convolutional neural network（卷积神经网络）	146
	neural network（神经网络）	118
	support vector machine（支持向量机）	104
功能	classification（分类）	577
	diagnosis（诊断）	476
	prediction（预测）	292
	feature selection（特征提取）	201
	segmentation（分割）	171

图8 2004—2023年关键词突现图谱注：蓝色线条表示时间间隔；红色线条表示出现年份和结束年份（突发的持续时间）；Strength（突发强度）表明关键词在此研究领域的重要性。

Figure 8 Keyword emergence graph from 2004 to 2023

参考文献 41

[1]	JACK C R Jr，BENNETT D A，BLENNOW K，et al. NIA-AA Research Framework：toward a biological definition of Alzheimer's disease[J]. Alzheimers Dement，2018，14（4）：535-562. DOI：10.1016/j.jalz.2018.02.018.
[2]	GUTIÉRREZ I L，DELLO RUSSO C，NOVELLINO F，et al. Noradrenaline in Alzheimer's disease：a new potential therapeutic target[J]. Int J Mol Sci，2022，23（11）：6143. DOI：10.3390/ijms23116143.
[3]	SCHELTENS P，DE STROOPER B，KIVIPELTO M，et al. Alzheimer's disease[J]. Lancet，2021，397（10284）：1577-1590. DOI：10.1016/S0140-6736(20)32205-4.
[4]	LEE G，NHO K，KANG B，et al. Predicting Alzheimer's disease progression using multi-modal deep learning approach[J]. Sci Rep，2019，9（1）：1952. DOI：10.1038/s41598-018-37769-z.
[5]	LI Z Y，JIANG X Q，WANG Y Z，et al. Applied machine learning in Alzheimer's disease research：omics，imaging，and clinical data[J]. Emerg Top Life Sci，2021，5（6）：765-777. DOI：10.1042/ETLS20210249.
[6]	GAO X R，CHIARIGLIONE M，QIN K，et al. Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction[J]. Sci Rep，2023，13（1）：450. DOI：10.1038/s41598-023-27551-1.
[7]	ALGHAMEDY F H，SHAFIQ M，LIU L J，et al. Machine learning-based multimodel computing for medical imaging for classification and detection of alzheimer disease[J]. Comput Intell Neurosci，2022，2022：9211477. DOI：10.1155/2022/9211477.
[8]	RAZA N，NASEER A，TAMOOR M，et al. Alzheimer disease classification through transfer learning approach[J]. Diagnostics，2023，13（4）：801. DOI：10.3390/diagnostics13040801.
[9]	MOYLE W，ARNAUTOVSKA U，OWNSWORTH T，et al. Potential of telepresence robots to enhance social connectedness in older adults with dementia：an integrative review of feasibility[J]. Int Psychogeriatr，2017，29（12）：1951-1964. DOI：10.1017/S1041610217001776.
[10]	SUK H I，LEE S W，SHEN D G，et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. NeuroImage，2014，101：569-582. DOI：10.1016/j.neuroimage.2014.06.077.
[11]	MORADI E，PEPE A，GASER C，et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. Neuroimage，2015，104：398-412. DOI：10.1016/j.neuroimage.2014.10.002.
[12]	LIU S Q，LIU S D，CAI W D，et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease[J]. IEEE Trans Biomed Eng，2015，62（4）：1132-1140. DOI：10.1109/TBME.2014.2372011.
[13]	KUMAR Y，KOUL A，SINGLA R，et al. Artificial intelligence in disease diagnosis：a systematic literature review，synthesizing framework and future research agenda[J]. J Ambient Intell Humaniz Comput，2023，14（7）：8459-8486. DOI：10.1007/s12652-021-03612-z.
[14]	MÁRQUEZ F，YASSA M A. Neuroimaging biomarkers for Alzheimer's disease[J]. Mol Neurodegener，2019，14（1）：21. DOI：10.1186/s13024-019-0325-5.
[15]	CARRILLO M C，ROWE C C，SZOEKE C，et al. Research and standardization in Alzheimer's trials：reaching international consensus[J]. Alzheimers Dement，2013，9（2）：160-168. DOI：10.1016/j.jalz.2012.10.006.
[16]	NGUYEN D T，RYU S，QURESHI M N I，et al. Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns[J]. PLoS One，2019，14（2）：e0212582. DOI：10.1371/journal.pone.0212582.
[17]	ETMINANI K，SOLIMAN A，DAVIDSSON A，et al. A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies，Alzheimer's disease，and mild cognitive impairment using brain 18F-FDG PET[J]. Eur J Nucl Med Mol Imaging，2022，49（2）：563-584. DOI：10.1007/s00259-021-05483-0.
[18]	ZHU W Y，SUN L，HUANG J S，et al. Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI[J]. IEEE Trans Med Imaging，2021，40（9）：2354-2366. DOI：10.1109/TMI.2021.3077079.
[19]	ZHANG D Q，WANG Y P，ZHOU L P，et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. NeuroImage，2011，55（3）：856-867. DOI：10.1016/j.neuroimage.2011.01.008.
[20]	SALEEM T J，ZAHRA S R，WU F，et al. Deep learning-based diagnosis of Alzheimer's disease[J]. J Pers Med，2022，12（5）：815. DOI：10.3390/jpm12050815.
[21]	KIM J S，HAN J W，BAE J B，et al. Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images：an empirical study[J]. Sci Rep，2022，12（1）：18007. DOI：10.1038/s41598-022-22917-3.
[22]	CHEUNG C Y，RAN A R，WANG S J，et al. A deep learning model for detection of Alzheimer's disease based on retinal photographs：a retrospective，multicentre case-control study[J]. Lancet Digit Health，2022，4（11）：e806-815. DOI：10.1016/S2589-7500(22)00169-8.
[23]	KIVIMÄKI M，LIVINGSTON G，SINGH-MANOUX A，et al. Estimating dementia risk using multifactorial prediction models[J]. JAMA Netw Open，2023，6（6）：e2318132. DOI：10.1001/jamanetworkopen.2023.18132.
[24]	YOU J，ZHANG Y R，WANG H F，et al. Development of a novel dementia risk prediction model in the general population：a large，longitudinal，population-based machine-learning study[J]. EClinicalMedicine，2022，53：101665. DOI：10.1016/j.eclinm.2022.101665.
[25]	ZHOU X P，CHEN Y，IP F C F，et al. Deep learning-based polygenic risk analysis for Alzheimer's disease prediction[J]. Commun Med，2023，3（1）：49. DOI：10.1038/s43856-023-00269-x.
[26]	YAGI T，KANEKIYO M，ITO J，et al. Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study[J]. Alzheimers Dement，2019，5：364-373. DOI：10.1016/j.trci.2019.06.004.
[27]	PETERSEN R C. Mild cognitive impairment as a diagnostic entity[J]. J Intern Med，2004，256（3）：183-194. DOI：10.1111/j.1365-2796.2004.01388.x.
[28]	LIN W M，GAO Q Q，YUAN J N，et al. Predicting Alzheimer's disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data[J]. Front Aging Neurosci，2020，12：77. DOI：10.3389/fnagi.2020.00077.
[29]	JO T，NHO K，SAYKIN A J. Deep learning in Alzheimer's disease：diagnostic classification and prognostic prediction using neuroimaging data[J]. Front Aging Neurosci，2019，11：220. DOI：10.3389/fnagi.2019.00220.
[30]	LUSTIG-BARZELAY Y，SHER I，SHARVIT-GINON I，et al. Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry[J]. Sci Rep，2022，12（1）：9945. DOI：10.1038/s41598-022-13999-0.
[31]	SHIMODA A，LI Y，HAYASHI H，et al. Dementia risks identified by vocal features via telephone conversations：a novel machine learning prediction model[J]. PLoS One，2021，16（7）：e0253988. DOI：10.1371/journal.pone.0253988.
[32]	VATANSEVER S，SCHLESSINGER A，WACKER D，et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases：state-of-the-arts and future directions[J]. Med Res Rev，2021，41（3）：1427-1473. DOI：10.1002/med.21764.
[33]	TSUJI S，HASE T，YACHIE-KINOSHITA A，et al. Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease[J]. Alzheimers Res Ther，2021，13（1）：92. DOI：10.1186/s13195-021-00826-3.
[34]	DAS B，MATHEW A T，BAIDYA A T K，et al. Artificial intelligence assisted identification of potential tau aggregation inhibitors：ligand- and structure-based virtual screening，in silico ADME，and molecular dynamics study[J]. Mol Divers，2023. DOI：10.1007/s11030-023-10645-3.
[35]	FANG J S，ZHANG P Y，WANG Q，et al. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease[J]. Alzheimers Res Ther，2022，14（1）：7. DOI：10.1186/s13195-021-00951-z.
[36]	SHU S，WOO B K. Use of technology and social media in dementia care：current and future directions[J]. World J Psychiatry，2021，11（4）：109-123. DOI：10.5498/wjp.v11.i4.109.
[37]	THYRIAN J R，HERTEL J，WUCHERER D，et al. Effectiveness and safety of dementia care management in primary care：a randomized clinical trial[J]. JAMA Psychiatry，2017，74（10）：996-1004. DOI：10.1001/jamapsychiatry.2017.2124.
[38]	MOON S，PARK K. The effect of digital reminiscence therapy on people with dementia：a pilot randomized controlled trial[J]. BMC Geriatr，2020，20（1）：166. DOI：10.1186/s12877-020-01563-2.
[39]	CAI X L，XIE D J，MADSEN K H，et al. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data[J]. Hum Brain Mapp，2020，41（1）：172-184. DOI：10.1002/hbm.24797.
[40]	LI Q，YANG M Q. Comparison of machine learning approaches for enhancing Alzheimer's disease classification[J]. PeerJ，2021，9：e10549. DOI：10.7717/peerj.10549.
[41]	AN N，DING H T，YANG J Y，et al. Deep ensemble learning for Alzheimer's disease classification[J]. J Biomed Inform，2020，105：103411. DOI：10.1016/j.jbi.2020.103411.

人工智能在阿尔茨海默病临床诊疗中的研究热点及前沿趋势分析

The Hotspots and Frontier Trends of Artificial Intelligence in the Clinical Diagnosis and Treatment of Alzheimer's Disease: Bibliometric Analysis of the Past 20 Years

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 41

相关文章 15

编辑推荐

Metrics

留言

[1]	谭丽博, 刘南阳, 李浩. 中成药治疗阿尔茨海默病临床研究的证据图分析[J]. 中国全科医学, 2026, 29(32): 4805-4814.
[2]	李姣, 向庆伟, 刘进进, 彭朗, 周剑杰, 杨琼, 梅应兵, 柳慧, 方英嵩, 谭子虎. 阿尔茨海默病患者跌倒相关住院风险预测模型的构建与验证研究[J]. 中国全科医学, 2026, 29(32): 4721-4729.
[3]	田向阳, 周迎生, 高莹. 糖尿病行为与生活方式干预展望[J]. 中国全科医学, 2026, 29(31): 4513-4520.
[4]	魏学娟, 王利华, 王丽, 任静林, 闫海青, 于海洋, 刘景峰. 智能语音外呼辅助传统干预对社区老年"三高"共病患者健康管理的效果研究：一项随机对照试验[J]. 中国全科医学, 2026, 29(31): 4635-4640.
[5]	谢远玲, 程丽, 龙湘玉, 苏政欣, 苏毅仪, 张人月, 孙国强, 陈旭霞, 周继兰. 人工智能眼科远程医疗诊断平台在社区眼健康筛查中的应用效果研究[J]. 中国全科医学, 2026, 29(31): 4641-4649.
[6]	黄牧, 张道春, 阳维, 钟丽明, 袁文静, 贾子琪, 谭相良, 段小慧, 刘岘, 陈维翠. 基于融合三维U-Net与Transformer的深度学习网络对腹部CT图像中结直肠癌病灶的自动分割：一项多中心、多设备研究[J]. 中国全科医学, 2026, 29(29): 4281-4287.
[7]	李姣, 周剑杰, 彭朗, 梅应兵, 谭子虎. 阿尔茨海默病合并社区获得性肺炎发生不良预后风险预测模型的构建与验证研究[J]. 中国全科医学, 2026, 29(29): 4248-4257.
[8]	陈毓婧, 王岩, 李光, 董本超, 杨培川, 马剑雄. 数字技术在亚洲地区老年人肌少症干预中应用的范围综述[J]. 中国全科医学, 2026, 29(29): 4200-4210.
[9]	陈澄, 陈开元, 孙乃玲, 曾华堂, 雷苏文, 梁万年. 人工智能赋能疾病预防控制标准化建设现状及建议[J]. 中国全科医学, 2026, 29(29): 4193-4199.
[10]	邓玮, 黄梦婷, 易胜利, 林杨, 许力丹, 任哲学, 朱秀琼, 李烨莎, 任梦鸽, 柯大智, 李桂琼, 吴志勤, 骆傲然, 何发培. AI赋能全科临床思维的培养与实施效果研究[J]. 中国全科医学, 2026, 29(28): 4078-4085.
[11]	解霄芸, 刘晨曦, 柳羽姝, 周艺涵. 中国基层医疗卫生机构人工智能研究实践及潜在数据贫困问题：一项范围综述[J]. 中国全科医学, 2026, 29(28): 4067-4077.
[12]	李海瑾, 朱宸立, 李卉, 冯郑文, 乔雨晨, 杜娟. 失智症诊断后管理相关指南的质量评价与内容分析[J]. 中国全科医学, 2026, 29(25): 3569-3576.
[13]	周雨涵, 闵钰琦, 郭洲, 杨敏, 岳露, 何美忱, 周海蓉, 王永晨. 人工智能时代全科医生的机遇与挑战[J]. 中国全科医学, 2026, 29(25): 3563-3568.
[14]	王洪川, 张剑波, 马伟, 赵司迪. 数字政策引领医疗数据空间的治理逻辑与生态构建：基于"技术-管理-法律"的分析框架[J]. 中国全科医学, 2026, 29(24): 3416-3422.
[15]	丘淑娴, 吴婉婷, 赵玫, 张海峰, 李涛, 于欣, 王华丽. 阿尔茨海默病知识量表在社区医生中的因子结构分析研究[J]. 中国全科医学, 2026, 29(23): 3336-3341.