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[1] Braak H, Braak E.Neuropathological stageing of Alzheimer-related changes[J]. Acta Neuropathol, 1991, 82(4):239-259. DOI:10.1007/BF00308809. [2] Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease[J]. Alzheimers Dement, 2011, 7(3):270-279. DOI:10.1016/j.jalz.2011.03.008. [3] Ward A, Tardiff S, Dye C, et al.Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic review of the literature[J]. Dement Geriatr Cogn Dis Extra, 2013, 3(1):320-332. DOI:10.1159/000354370. [4] 潘惠英, 王君俏, 吴美玲. 社区老年轻度认知障碍患者疾病认知水平调查及影响因素分析[J]. 护理学报, 2011, 18(21):1-4. DOI:10.16460/j.issn1008-9969.2011.21.024. [5] 陈绍敏, 王英. 轻度认知功能障碍老年人阿尔茨海默病风险预测的研究进展[J].实用老年医学, 2021, 35(12):1304-1308. DOI:10.3969/j.issn.1003-9198.2021.12.025. [6] Grant SW, Collins GS, Nashef SAM.Statistical primer: developing and validating a risk prediction model[J]. Eur J Cardiothorac Surg, 2018, 54(2):203-208. DOI:10.1093/ejcts/ezy180. [7] Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J]. PLoS Med, 2014, 11(10): e1001744. DOI: 10.1371/journal.pmed.1001744. [8] Moons KGM, Wolff RF, Riley RD, et al.PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration[J]. Ann Intern Med, 2019, 170(1):W1-W33. DOI:10.7326/M18-1377. [9] 智丽萍, 祝昭, 袁敏. 非模型-融合影像、神经认知评价和生物标志等多模态数据预测阿尔兹海默症进展阶段及转化[J]. 南京医科大学学报(自然科学版), 2022, 42(4):522-528. DOI:10.7655/NYDXBNS20220410. [10] 张嘉嘉, 秦瑶, 韩红娟, 等. 基于Landmark模型动态预测老年人轻度认知障碍向阿尔茨海默病的转化[J]. 中国卫生统计, 2022, 39(4):534-537.DOI:10.3969/j.issn.1002-3674.2022.04.011. [11] 宋娆, 吴小佳, 李传明, 等. 基于MRI影像组学及ATN分类系统的列线图预测轻度认知障碍进展[J].放射学实践, 2021, 36(12):1481-1487. DOI:10.13609/j.cnki.1000-0313.2021.12.005. [12] 庄丽英, 赖其伦, 刘璐, 等. Framingham风险评分对中老年轻度认知障碍患者进展为痴呆的预测价值[J]. 中华老年病研究电子杂志, 2021, 8(3):20-23. [13] 丛慧文, 徐雅琪, 王爱民, 等. XGBoost算法在轻度认知障碍人群阿尔兹海默病发病预测中的应用[J]. 郑州大学学报(医学版), 2022, 57(6):751-756. DOI:10.13705/j.issn.1671-6825.2021.12.068. [14] Barnes DE, Cenzer IS, Yaffe K, et al.A point-based tool to predict conversion from MCI to probable Alzheimer’s disease[J]. Alzheimers Dement, 2014,10(6):646-655. DOI:10.1016/j.jalz.2013.12.014. [15] Liu, Liu H, Lutz M, et al. Association between polygenic risk score and the progression from mild cognitive impairment to Alzheimer’s disease[J]. J Alzheimers Dis, 2021, 84(3):1323-1335. DOI:10.3233/JAD-210700. [16] Zheng W, Yao Z, Li Y, et al.Brain connectivity based prediction of Alzheimer’s disease in patients with mild cognitive impairment based on multi-modal images[J]. Front Hum Neurosci, 2019(13):399. DOI:10.3389/fnhum.2019.00399. [17] Mazzeo S, Santangelo R, Bernasconi MP, et al.Combining cerebrospinal fluid biomarkers and neuropsychological assessment: a simple and cost-effective algorithm to predict the progression from mild cognitive impairment to Alzheimer’s disease dementia[J]. J Alzheimers Dis, 2016, 54(4):1495-1508. DOI:10.3233/JAD-160360. [18] Zhou P, Zeng R, Yu L, et al.Deep-learning radiomics for discrimination conversion of Alzheimer’s disease in patients with mild cognitive impairment: a study based on 18F-FDG PET imaging[J]. Front Aging Neurosci,2021(13): 764872. DOI:10.3389/fnagi.2021.764872. [19] Wang M, Chekouo T, Ismail Z, et al.Elicited clinician knowledge did not improve dementia risk prediction in individuals with mild cognitive impairment[J]. J Clin Epidemiol, 2023(158):111-118. DOI:10.1016/j.jclinepi.2023.03.009. [20] Rodríguez-Rodríguez E, Sánchez-Juan P,Vázquez-Higuera JL, et al.Genetic risk score predicting accelerated progression from mild cognitive impairment to Alzheimer’s disease[J]. J Neural Transm, 2013, 120(5):807-812. DOI:10.1007/s00702-012-0920-x. [21] Kikuchi M, Kobayashi K, Itoh S, et al.Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data[J]. Comput Struct Biotechnol J, 2022(20):5296-5308. DOI:10.1016/j.csbj.2022.08.007. [22] 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. [23] Chen J, Chen G, Shu H, et al.Predicting progression from mild cognitive impairment to Alzheimer’s disease on an individual subject basis by applying the CARE index across different independent cohorts[J].Aging (Albany NY), 2019, 11(8):2185-2201. DOI:10.18632/aging.101883. [24] Handels RLH, Vos SJB, Kramberger MG, et al.Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers[J]. Alzheimers Dement, 2017, 13(8):903-912. DOI:10.1016/j.jalz.2016.12.015. [25] Liu K, Chen K, Yao L, et al.Prediction of mild cognitive impairment conversion using a combination of independent component analysis and the Cox model[J].Front Hum Neurosci, 2017(11):33. DOI:10.3389/fnhum.2017.00033. [26] Sörensen A, Blazhenets G, Rücker G, et al.Prognosis of conversion of mild cognitive impairment to Alzheimer’s dementia by voxel-wise Cox regression based on FDG PET data[J]. Neuroimage Clin, 2019(21):101637. DOI: 10.1016/j.nicl.2018.101637. [27] Nezhadmoghada F, Martinez-Torteya A, Trevi?o V, et al. Robust discovery of mild cognitive impairment subtypes and their risk of Alzheimer’s disease conversion using unsupervised machine learning and gaussian mixture modeling[J]. Curr Alzheimer Res, 2021,18(7):595-606. DOI:10.2174/1567205018666210831145825. [28] Pena D, Suescun J, Schiess M, et al.Toward a multimodal computer-aided diagnostic tool for Alzheimer’s disease conversion[J]. Front Neurosci, 2022(15):744190. DOI:10.3389/fnins.2021.744190. [29] Cooke E, Smith V, Brenner M.Parents’ experiences of accessing respite care for children with Autism Spectrum Disorder (ASD) at the acute and primary care interface: a systematic review[J]. BMC Pediatr, 2020, 20(1): 244. DOI:10.1186/s12887-020-02045-5. [30] 陈香萍, 张奕, 庄一渝, 等. PROBAST:诊断或预后多因素预测模型研究偏倚风险的评估工具[J]. 中国循证医学杂志,2020,20(6):737-744.DOI:10.7507/1672-2531.201910087. [31] 张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立[J]. 中国卫生统计, 2015, 32(4):724-726. [32] 谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型:模型的建立[J]. 中国循证心血管医学杂志, 2019, 11(1):14-16;23. DOI: 10.3969/j.issn.1674-4055.2019.01.04. [33] 王俊峰, 章仲恒, 周支瑞, 等. 临床预测模型:模型的验证[J]. 中国循证心血管医学杂志,2019,11(2):141-144. DOI: 10.3969/j.issn.1674-4055.2019.02.04. [34] 陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读[J]. 中华医学杂志, 2018, 98(44):3556-3560.DOI:10.3760/cma.j.issn.0376-2491.2018.44.002. [35] 孙雯倩, 林榕, 颜缘娇, 等. 老年认知障碍风险预测模型的研究进展[J]. 中国护理管理, 2023, 23(8): 1263-1267. DOI: 10.3969/j.issn.1672-1756.2023.08.030 [36] Hastie T, Tibshirani R, Friedman J.The elements of statistical learning: data mining, inference, and prediction[M]. New York: Springer, 2009: 1-8. DOI:10.1007/978-0-387-84858-7_1. [37] Ng K, Sun J, Hu J, et al.Personalized predictive modeling and risk factor identification using patient similarity[J]. AMIA Jt Summits Transl Sci Proc, 2015(2015):132-136. [38] Jia L, Quan M, Fu Y, et al.Dementia in China: epidemiology, clinical management,and research advances[J]. Lancet Neurol, 2020, 19(1):81-92. DOI:10.1016/S1474-4422(19)30290-X. |
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