{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:01:55Z","timestamp":1791914515405,"version":"4.3.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/R502248\/1"],"award-info":[{"award-number":["MR\/R502248\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/M020533\/1"],"award-info":[{"award-number":["EP\/M020533\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"name":"brc"},{"name":"Health Data Research UK"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer\u2019s disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42\u201373) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-021-01693-6","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T05:06:35Z","timestamp":1638939995000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Identifying and evaluating clinical subtypes of Alzheimer\u2019s disease in care electronic health records using unsupervised machine learning"],"prefix":"10.1186","volume":"21","author":[{"given":"Nonie","family":"Alexander","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C.","family":"Alexander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spiros","family":"Denaxas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"issue":"2","key":"1693_CR1","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3233\/JAD-170665","volume":"61","author":"C Ferrari","year":"2017","unstructured":"Ferrari C, Lombardi G, Polito C, Lucidi G, Bagnoli S, Piaceri I, et al. Alzheimer\u2019s disease progression: factors influencing cognitive decline. J Alzheimers Dis. 2017;61(2):785\u201391.","journal-title":"J Alzheimers Dis"},{"issue":"1","key":"1693_CR2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13195-017-0294-2","volume":"9","author":"C Wattmo","year":"2017","unstructured":"Wattmo C, Wallin \u00c5K. Early-versus late-onset Alzheimer\u2019s disease in clinical practice: cognitive and global outcomes over 3 years. Alzheimers Res Ther. 2017;9(1):70.","journal-title":"Alzheimers Res Ther"},{"issue":"1","key":"1693_CR3","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1159\/000265552","volume":"29","author":"R Ravona-Springer","year":"2010","unstructured":"Ravona-Springer R, Luo X, Schmeidler J, Wysocki M, Lesser G, Rapp M, et al. Diabetes is associated with increased rate of cognitive decline in questionably demented elderly. Dement Geriatr Cogn Disord. 2010;29(1):68\u201374.","journal-title":"Dement Geriatr Cogn Disord"},{"key":"1693_CR4","doi-asserted-by":"publisher","DOI":"10.4172\/Neuropsychiatry.1000479","author":"PJ Modrego","year":"2018","unstructured":"Modrego PJ, Lobo A. Determinants of progression and mortality in Alzheimers disease: a systematic review. Neuropsychiatry. 2018. https:\/\/doi.org\/10.4172\/Neuropsychiatry.1000479.","journal-title":"Neuropsychiatry"},{"key":"1693_CR5","first-page":"536","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2013","unstructured":"Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Berlin: Springer; 2013. p. 536."},{"issue":"2","key":"1693_CR6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1093\/clinchem\/38.2.182","volume":"38","author":"W Vogt","year":"1992","unstructured":"Vogt W, Nagel D. Cluster analysis in diagnosis. Clin Chem. 1992;38(2):182\u201398.","journal-title":"Clin Chem"},{"issue":"2","key":"1693_CR7","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1017\/S0033291700027495","volume":"24","author":"RG Morris","year":"1994","unstructured":"Morris RG. Modular deficits in Alzheimer-type dementia. Edited by M. F. Schwartz. (Pp. 346; \u00a340.50.) MIT Press: London. 1990. Psychol Med. 1994;24(2):529\u2013529.","journal-title":"Psychol Med"},{"issue":"190","key":"1693_CR8","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jad.2015.09.069","volume":"15","author":"Q-F Zhao","year":"2016","unstructured":"Zhao Q-F, Tan L, Wang H-F, Jiang T, Tan M-S, Tan L, et al. The prevalence of neuropsychiatric symptoms in Alzheimer\u2019s disease: systematic review and meta-analysis. J Affect Disord. 2016;15(190):264\u201371.","journal-title":"J Affect Disord"},{"issue":"4","key":"1693_CR9","doi-asserted-by":"crossref","first-page":"e10246","DOI":"10.1002\/lrh2.10246","volume":"4","author":"J Xu","year":"2020","unstructured":"Xu J, Wang F, Xu Z, Adekkanattu P, Brandt P, Jiang G, et al. Data-driven discovery of probable Alzheimer\u2019s disease and related dementia subphenotypes using electronic health records. Learn Health Syst. 2020;4(4):e10246.","journal-title":"Learn Health Syst"},{"issue":"3","key":"1693_CR10","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41746-020-0301-z","volume":"17","author":"I Landi","year":"2020","unstructured":"Landi I, Glicksberg BS, Lee H-C, Cherng S, Landi G, Danieletto M, et al. Deep representation learning of electronic health records to unlock patient stratification at scale. NPJ Digit Med. 2020;17(3):96.","journal-title":"NPJ Digit Med"},{"issue":"11","key":"1693_CR11","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1016\/j.jalz.2017.03.002","volume":"13","author":"NME Scheltens","year":"2017","unstructured":"Scheltens NME, Tijms BM, Koene T, Barkhof F, Teunissen CE, Wolfsgruber S, et al. Cognitive subtypes of probable Alzheimer\u2019s disease robustly identified in four cohorts. Alzheimers Dement. 2017;13(11):1226\u201336.","journal-title":"Alzheimers Dement"},{"issue":"3","key":"1693_CR12","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1136\/jnnp-2014-309582","volume":"87","author":"NME Scheltens","year":"2016","unstructured":"Scheltens NME, Galindo-Garre F, Pijnenburg YAL, van der Vlies AE, Smits LL, Koene T, et al. The identification of cognitive subtypes in Alzheimer\u2019s disease dementia using latent class analysis. J Neurol Neurosurg Psychiatry. 2016;87(3):235\u201343.","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"7","key":"1693_CR13","doi-asserted-by":"crossref","first-page":"565","DOI":"10.2147\/NDT.S24196","volume":"30","author":"AK Wallin","year":"2011","unstructured":"Wallin AK, Wattmo C, Minthon L. Galantamine treatment in Alzheimer\u2019s disease: response and long-term outcome in a routine clinical setting. Neuropsychiatr Dis Treat. 2011;30(7):565\u201376.","journal-title":"Neuropsychiatr Dis Treat"},{"key":"1693_CR14","doi-asserted-by":"publisher","first-page":"833","DOI":"10.3233\/jad-150407","volume":"48","author":"CC Price","year":"2015","unstructured":"Price CC, Tanner JJ, Schmalfuss IM, Brumback B, Heilman KM, Libon DJ. Dissociating statistically-determined Alzheimer\u2019s disease\/vascular dementia neuropsychological syndromes using white and gray neuroradiological parameters. J Alzheimer\u2019s Dis. 2015;48:833\u201347. https:\/\/doi.org\/10.3233\/jad-150407.","journal-title":"J Alzheimer\u2019s Dis"},{"issue":"2","key":"1693_CR15","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1017\/S1355617709991160","volume":"16","author":"JE Davidson","year":"2010","unstructured":"Davidson JE, Irizarry MC, Bray BC, Wetten S, Galwey N, Gibson R, et al. An exploration of cognitive subgroups in Alzheimer\u2019s disease. J Int Neuropsychol Soc. 2010;16(2):233\u201343.","journal-title":"J Int Neuropsychol Soc"},{"issue":"3","key":"1693_CR16","doi-asserted-by":"crossref","first-page":"999","DOI":"10.3233\/JAD-132147","volume":"42","author":"DJ Libon","year":"2014","unstructured":"Libon DJ, Drabick DAG, Giovannetti T, Price CC, Bondi MW, Eppig J, et al. Neuropsychological syndromes associated with Alzheimer\u2019s\/vascular dementia: a latent class analysis. J Alzheimers Dis. 2014;42(3):999\u20131014.","journal-title":"J Alzheimers Dis"},{"issue":"Pt B","key":"1693_CR17","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neuroimage.2016.02.041","volume":"145","author":"E Varol","year":"2017","unstructured":"Varol E, Sotiras A, Davatzikos C, Alzheimer\u2019s Disease Neuroimaging Initiative. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage. 2017;145(Pt B):346\u201364.","journal-title":"Neuroimage"},{"issue":"2","key":"1693_CR18","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TMI.2015.2487423","volume":"35","author":"A Dong","year":"2016","unstructured":"Dong A, Honnorat N, Gaonkar B, Davatzikos C. CHIMERA: clustering of heterogeneous disease effects via distribution matching of imaging patterns. IEEE Trans Med Imaging. 2016;35(2):612\u201321.","journal-title":"IEEE Trans Med Imaging"},{"key":"1693_CR19","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neurobiolaging.2018.01.009","volume":"65","author":"K Poulakis","year":"2018","unstructured":"Poulakis K, Pereira JB, Mecocci P, Vellas B, Tsolaki M, K\u0142oszewska I, et al. Heterogeneous patterns of brain atrophy in Alzheimer\u2019s disease. Neurobiol Aging. 2018;65:98\u2013108.","journal-title":"Neurobiol Aging"},{"key":"1693_CR20","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1155\/1994\/325374","volume":"7","author":"H F\u00f6rstl","year":"1994","unstructured":"F\u00f6rstl H, Levy R, Burns A, Luthert P, Cairns N. Pathways and patterns of cell loss in verified Alzheimer\u2019s disease: a factor and cluster analysis of clinico-pathological subgroups. Behav Neurol. 1994;7:175\u201380. https:\/\/doi.org\/10.1155\/1994\/325374.","journal-title":"Behav Neurol"},{"key":"1693_CR21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jocn.2016.01.025","volume":"30","author":"CB Malpas","year":"2016","unstructured":"Malpas CB. Structural neuroimaging correlates of cognitive status in older adults: a person-oriented approach. J Clin Neurosci. 2016;30:77\u201382.","journal-title":"J Clin Neurosci"},{"issue":"21","key":"1693_CR22","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1212\/WNL.0000000000001003","volume":"83","author":"Y Noh","year":"2014","unstructured":"Noh Y, Jeon S, Lee JM, Seo SW, Kim GH, Cho H, et al. Anatomical heterogeneity of Alzheimer disease: based on cortical thickness on MRIs. Neurology. 2014;83(21):1936\u201344.","journal-title":"Neurology"},{"key":"1693_CR23","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.3233\/jad-150244","volume":"49","author":"G Tosto","year":"2015","unstructured":"Tosto G, Monsell SE, Hawes SE, Bruno G, Mayeux R. Progression of extrapyramidal signs in Alzheimer\u2019s disease: clinical and neuropathological correlates. J Alzheimer\u2019s Dis. 2015;49:1085\u201393. https:\/\/doi.org\/10.3233\/jad-150244.","journal-title":"J Alzheimer\u2019s Dis"},{"issue":"12","key":"1693_CR24","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1093\/brain\/awy264","volume":"141","author":"M ten Kate","year":"2018","unstructured":"ten Kate M, Dicks E, Visser PJ, van der Flier WM, Teunissen CE, Barkhof F, et al. Atrophy subtypes in prodromal Alzheimer\u2019s disease are associated with cognitive decline. Brain. 2018;141(12):3443\u201356.","journal-title":"Brain"},{"issue":"7","key":"1693_CR25","doi-asserted-by":"crossref","first-page":"e40996","DOI":"10.1371\/journal.pone.0040996","volume":"7","author":"H-M Wang","year":"2012","unstructured":"Wang H-M, Hsiao C-L, Hsieh A-R, Lin Y-C, Fann CSJ. Constructing endophenotypes of complex diseases using non-negative matrix factorization and adjusted rand index. PLoS ONE. 2012;7(7):e40996.","journal-title":"PLoS ONE"},{"issue":"2","key":"1693_CR26","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1002\/ana.25142","volume":"83","author":"JL Whitwell","year":"2018","unstructured":"Whitwell JL, Graff-Radford J, Tosakulwong N, Weigand SD, Machulda M, Senjem ML, et al. [18F] AV-1451 clustering of entorhinal and cortical uptake in Alzheimer\u2019s disease. Ann Neurol. 2018;83(2):248\u201357.","journal-title":"Ann Neurol"},{"key":"1693_CR27","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1038\/s41591-021-01309-6","volume":"27","author":"JW Vogel","year":"2021","unstructured":"Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, Joie RL, Aksman LM, Grothe MJ, Iturria-Medina Y, the Alzheimer\u2019s Disease Neuroimaging Initiative*, Pontecorvo MJ, Devous MD, Rabinovici GD, Alexander DC, Lyoo CH, Evans AC, Hansson O. Four distinct trajectories of tau deposition identified in Alzheimer\u2019s disease. Nat Med. 2021;27:871\u201381.","journal-title":"Nat Med"},{"issue":"1","key":"1693_CR28","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","volume":"9","author":"AL Young","year":"2018","unstructured":"Young AL, Marinescu RV, Oxtoby NP, Bocchetta M, Yong K, Firth NC, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun. 2018;9(1):4273.","journal-title":"Nat Commun"},{"issue":"2","key":"1693_CR29","doi-asserted-by":"crossref","first-page":"651","DOI":"10.3233\/JAD-140261","volume":"42","author":"J Peter","year":"2014","unstructured":"Peter J, Abdulkadir A, Kaller C, K\u00fcmmerer D, H\u00fcll M, Vach W, et al. Subgroups of Alzheimer\u2019s disease: stability of empirical clusters over time. J Alzheimers Dis. 2014;42(2):651\u201361.","journal-title":"J Alzheimers Dis"},{"issue":"3","key":"1693_CR30","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1037\/neu0000159","volume":"29","author":"L Yu","year":"2015","unstructured":"Yu L, Boyle PA, Segawa E, Leurgans S, Schneider JA, Wilson RS, et al. Residual decline in cognition after adjustment for common neuropathologic conditions. Neuropsychology. 2015;29(3):335\u201343.","journal-title":"Neuropsychology"},{"key":"1693_CR31","volume-title":"CPRD GOLD data specification","author":"S Padmanabhan","year":"2017","unstructured":"Padmanabhan S. CPRD GOLD data specification. CPRD; 2017."},{"issue":"3","key":"1693_CR32","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1093\/ije\/dyv098","volume":"44","author":"E Herrett","year":"2015","unstructured":"Herrett E, Gallagher AM, Bhaskaran K, Forbes H, Mathur R, van Staa T, et al. Data resource profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol. 2015;44(3):827\u201336.","journal-title":"Int J Epidemiol"},{"key":"1693_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz105","author":"S Denaxas","year":"2019","unstructured":"Denaxas S, Gonzalez-Izquierdo A, Direk K, Fitzpatrick NK, Fatemifar G, Banerjee A, et al. UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc. 2019. https:\/\/doi.org\/10.1093\/jamia\/ocz105.","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"1693_CR34","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1001\/jama.288.12.1475","volume":"288","author":"CG Lyketsos","year":"2002","unstructured":"Lyketsos CG, Lopez O, Jones B, Fitzpatrick AL, Breitner J, DeKosky S. Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment: results from the cardiovascular health study. JAMA. 2002;288(12):1475\u201383.","journal-title":"JAMA"},{"issue":"11","key":"1693_CR35","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1176\/ajp.141.11.1356","volume":"141","author":"WG Rosen","year":"1984","unstructured":"Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer\u2019s disease. Am J Psychiatry. 1984;141(11):1356\u201364.","journal-title":"Am J Psychiatry"},{"issue":"11","key":"1693_CR36","first-page":"625","volume":"91","author":"FM Baker","year":"1999","unstructured":"Baker FM, Wiley C, Kokmen E, Chandra V, Schoenberg BS. Delirium episodes during the course of clinically diagnosed Alzheimer\u2019s disease. J Natl Med Assoc. 1999;91(11):625\u201330.","journal-title":"J Natl Med Assoc"},{"issue":"8","key":"1693_CR37","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1001\/archneurol.2009.130","volume":"66","author":"N Scarmeas","year":"2009","unstructured":"Scarmeas N, Honig LS, Choi H, Cantero J, Brandt J, Blacker D, et al. Seizures in Alzheimer disease: who, when, and how common? Arch Neurol. 2009;66(8):992\u20137.","journal-title":"Arch Neurol"},{"issue":"6","key":"1693_CR38","doi-asserted-by":"crossref","first-page":"e0199026","DOI":"10.1371\/journal.pone.0199026","volume":"13","author":"M Pujades-Rodriguez","year":"2018","unstructured":"Pujades-Rodriguez M, Assi V, Gonzalez-Izquierdo A, Wilkinson T, Schnier C, Sudlow C, et al. The diagnosis, burden and prognosis of dementia: a record-linkage cohort study in England. PLoS ONE. 2018;13(6):e0199026.","journal-title":"PLoS ONE"},{"issue":"2","key":"1693_CR39","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1159\/000478978","volume":"7","author":"S Kushwaha","year":"2017","unstructured":"Kushwaha S, Talwar P, Anthony A, Gupta M, Bala K, Agarwal R, et al. Clinical spectrum, risk factors, and behavioral abnormalities among dementia subtypes in a North Indian population: a hospital-based study. Dement Geriatr Cogn Dis Extra. 2017;7(2):257\u201373.","journal-title":"Dement Geriatr Cogn Dis Extra"},{"key":"1693_CR40","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1136\/jnnp-2014-310095","volume":"87","author":"J-Q Li","year":"2016","unstructured":"Li J-Q, Tan L, Wang H-F, Tan M-S, Tan L, Xu W, et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease: a systematic review and meta-analysis of cohort studies. J Neurol Neurosurg Psychiatry. 2016;87:476\u201384. https:\/\/doi.org\/10.1136\/jnnp-2014-310095.","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"3","key":"1693_CR41","doi-asserted-by":"crossref","first-page":"e0194990","DOI":"10.1371\/journal.pone.0194990","volume":"13","author":"N Assuncao","year":"2018","unstructured":"Assuncao N, Sudo FK, Drummond C, de Felice FG, Mattos P. Metabolic syndrome and cognitive decline in the elderly: a systematic review. PLoS ONE. 2018;13(3):e0194990.","journal-title":"PLoS ONE"},{"issue":"17","key":"1693_CR42","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1212\/WNL.0b013e318217e7a4","volume":"76","author":"J Li","year":"2011","unstructured":"Li J, Wang YJ, Zhang M, Xu ZQ, Gao CY, Fang CQ, et al. Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology. 2011;76(17):1485\u201391.","journal-title":"Neurology"},{"issue":"1","key":"1693_CR43","doi-asserted-by":"crossref","first-page":"56","DOI":"10.4103\/0028-3886.173623","volume":"64","author":"P Ungprasert","year":"2016","unstructured":"Ungprasert P, Wijarnpreecha K, Thongprayoon C. Rheumatoid arthritis and the risk of dementia: a systematic review and meta-analysis. Neurol India. 2016;64(1):56\u201361.","journal-title":"Neurol India"},{"issue":"2","key":"1693_CR44","doi-asserted-by":"crossref","first-page":"479","DOI":"10.3233\/JAD-140666","volume":"43","author":"J Zhou","year":"2015","unstructured":"Zhou J, Yu J-T, Wang H-F, Meng X-F, Tan C-C, Wang J, et al. Association between stroke and Alzheimer\u2019s disease: systematic review and meta-analysis. J Alzheimers Dis. 2015;43(2):479\u201389.","journal-title":"J Alzheimers Dis"},{"issue":"5","key":"1693_CR45","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1159\/000338135","volume":"35","author":"T Etgen","year":"2012","unstructured":"Etgen T, Chonchol M, F\u00f6rstl H, Sander D. Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. Am J Nephrol. 2012;35(5):474\u201382.","journal-title":"Am J Nephrol"},{"issue":"12","key":"1693_CR46","first-page":"1299","volume":"86","author":"W Xu","year":"2015","unstructured":"Xu W, Tan L, Wang H-F, Jiang T, Tan M-S, Tan L, et al. Meta-analysis of modifiable risk factors for Alzheimer\u2019s disease. J Neurol Neurosurg Psychiatry. 2015;86(12):1299\u2013306.","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"2","key":"1693_CR47","doi-asserted-by":"crossref","first-page":"565","DOI":"10.3233\/JAD-140168","volume":"42","author":"L-L Ma","year":"2014","unstructured":"Ma L-L, Yu J-T, Wang H-F, Meng X-F, Tan C-C, Wang C, et al. Association between cancer and Alzheimer\u2019s disease: systematic review and meta-analysis. J Alzheimers Dis. 2014;42(2):565\u201373.","journal-title":"J Alzheimers Dis"},{"issue":"4","key":"1693_CR48","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1159\/000253845","volume":"28","author":"I Villar-Fern\u00e1ndez","year":"2009","unstructured":"Villar-Fern\u00e1ndez I, Bjerrum L, Feja C, Rabanaque MJ. Variability in the prescription of cholinesterase inhibitors and memantine. Dement Geriatr Cogn Disord. 2009;28(4):373\u20139.","journal-title":"Dement Geriatr Cogn Disord"},{"key":"1693_CR49","volume-title":"Dementia: Assessment, management and support for people living with dementia and their carers","author":"National Institute for Health and Care Excellence (UK)","year":"2018","unstructured":"National Institute for Health and Care Excellence (UK). Dementia: Assessment, management and support for people living with dementia and their carers. London: National Institute for Health and Care Excellence (UK); 2018."},{"key":"1693_CR50","doi-asserted-by":"crossref","first-page":"115","DOI":"10.4135\/9781412993906","volume-title":"Multiple correspondence analysis","author":"B Le Roux","year":"2010","unstructured":"Le Roux B, Rouanet H. Multiple correspondence analysis. SAGE; 2010. p. 115."},{"issue":"20","key":"1693_CR51","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"1","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;1(20):53\u201365.","journal-title":"J Comput Appl Math"},{"key":"1693_CR52","doi-asserted-by":"crossref","unstructured":"Chen SS, Gopalakrishnan PS. Clustering via the Bayesian information criterion with applications in speech recognition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP \u201998 (Cat No98CH36181), vol. 2. 1998. p. 645\u20138.","DOI":"10.1109\/ICASSP.1998.675347"},{"key":"1693_CR53","doi-asserted-by":"crossref","unstructured":"Couto J. Kernel K-means for categorical data. In: Advances in intelligent data analysis VI. Berlin: Springer; 2005. p. 46\u201356.","DOI":"10.1007\/11552253_5"},{"issue":"5814","key":"1693_CR54","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972\u20136.","journal-title":"Science"},{"key":"1693_CR55","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1017\/CBO9780511499531","volume-title":"Applied latent class analysis","author":"JA Hagenaars","year":"2002","unstructured":"Hagenaars JA, McCutcheon AL. Applied latent class analysis. Cambridge University Press; 2002. p. 454."},{"issue":"Pt 3","key":"1693_CR56","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1093\/brain\/123.3.484","volume":"123","author":"CJ Galton","year":"2000","unstructured":"Galton CJ, Patterson K, Xuereb JH, Hodges JR. Atypical and typical presentations of Alzheimer\u2019s disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases. Brain. 2000;123(Pt 3):484\u201398.","journal-title":"Brain"},{"issue":"1","key":"1693_CR57","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.csda.2006.11.025","volume":"52","author":"C Hennig","year":"2007","unstructured":"Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal. 2007;52(1):258\u201371.","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"1693_CR58","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s12911-019-0805-0","volume":"19","author":"M Pikoula","year":"2019","unstructured":"Pikoula M, Quint JK, Nissen F, Hemingway H, Smeeth L, Denaxas S. Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records. BMC Med Inform Decis Mak. 2019;19(1):86.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1693_CR59","doi-asserted-by":"crossref","first-page":"e1442","DOI":"10.1136\/bmj.e1442","volume":"344","author":"JA Driver","year":"2012","unstructured":"Driver JA, Beiser A, Au R, Kreger BE, Splansky GL, Kurth T, et al. Inverse association between cancer and Alzheimer\u2019s disease: results from the Framingham Heart Study. BMJ. 2012;344:e1442.","journal-title":"BMJ"},{"issue":"20","key":"1693_CR60","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1212\/01.wnl.0000345062.86148.3f","volume":"72","author":"MDM Haag","year":"2009","unstructured":"Haag MDM, Hofman A, Koudstaal PJ, Breteler MMB, Stricker BHC. Duration of antihypertensive drug use and risk of dementia: a prospective cohort study. Neurology. 2009;72(20):1727\u201334.","journal-title":"Neurology"},{"key":"1693_CR61","doi-asserted-by":"crossref","unstructured":"Huang Y, Axsom KM, Lee J, Subramanian L, Zhang Y. DICE: deep significance clustering for outcome-aware stratification. arXiv [cs.LG]. 2021. arXiv:2101.02344.","DOI":"10.1101\/2020.10.04.20204321"},{"key":"1693_CR62","first-page":"457","volume":"264","author":"E Xia","year":"2019","unstructured":"Xia E, Du X, Mei J, Sun W, Tong S, Kang Z, et al. Outcome-driven clustering of acute coronary syndrome patients using multi-task neural network with attention. Stud Health Technol Inform. 2019;264:457\u201361.","journal-title":"Stud Health Technol Inform"},{"key":"1693_CR63","unstructured":"Lee C, Schaar M. Temporal Phenotyping using Deep Predictive Clustering of Disease Progression. ICML. 2020."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01693-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-021-01693-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01693-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T20:24:51Z","timestamp":1726259091000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-021-01693-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1693"],"URL":"https:\/\/doi.org\/10.1186\/s12911-021-01693-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"22 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The protocol for this research was approved by the Independent Scientific Advisory Committee (ISAC) for MHRA Database Research (protocol number 18_111) prior to receipt of data. Generic ethical approval for observational research using the CPRD with approval from ISAC has been granted by a Health Research Authority (HRA) Research Ethics Committee (East Midlands \u2013 Derby, REC reference number 05\/MRE04\/87).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"343"}}