{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:25:59Z","timestamp":1781191559824,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Health and Medical Research Council of Australia","award":["350833"],"award-info":[{"award-number":["350833"]}]},{"name":"National Health and Medical Research Council of Australia","award":["350833"],"award-info":[{"award-number":["350833"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-024-06018-8","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T02:02:37Z","timestamp":1739844157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Identifying risk factors for Alzheimer\u2019s disease from multivariate longitudinal clinical data using temporal pattern mining"],"prefix":"10.1186","volume":"26","author":[{"given":"Annette","family":"Spooner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gelareh","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perminder S.","family":"Sachdev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Brodaty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arcot","family":"Sowmya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"6018_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jbi.2016.11.006","volume":"65","author":"J Zhao","year":"2017","unstructured":"Zhao J, Papapetrou P, Asker L, Bostr\u00f6m H. Learning from heterogeneous temporal data in electronic health records. J Biomed Inform. 2017;65:105\u201319. https:\/\/doi.org\/10.1016\/j.jbi.2016.11.006.","journal-title":"J Biomed Inform"},{"key":"6018_CR2","unstructured":"Senin P. Dynamic time warping algorithm review. Science (80) [Internet]. 2008; 2007(December):1\u201323. Available from: http:\/\/129.173.35.31\/~pf\/Linguistique\/Treillis\/ReviewDTW.pdf"},{"key":"6018_CR3","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. Tech rep ICS 8504 San Diego, Calif Inst Cogn Sci Univ Calif. 1985","DOI":"10.21236\/ADA164453"},{"key":"6018_CR4","unstructured":"Agarwal R, Srikant R. Fast algorithms for mining association rules. In: proceedings 20th international conference very large data bases, Santiago Chile, 12\u201315 1994, 487\u2013499. vol. 2(12) pp. 13\u201324. 1994"},{"issue":"2","key":"6018_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","volume":"8","author":"J Han","year":"2004","unstructured":"Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation\u2014a Frequent-pattern tree approach. Data Min Knowl Discov. 2004;8(2):53\u201387.","journal-title":"Data Min Knowl Discov"},{"issue":"11","key":"6018_CR6","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TKDE.2004.77","volume":"16","author":"J Pei","year":"2004","unstructured":"Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, et al. Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng. 2004;16(11):1424\u201340.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"6018_CR7","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TKDE.2007.190613","volume":"19","author":"SY Wu","year":"2007","unstructured":"Wu SY, Chen YL. Mining nonambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng. 2007;19(6):742\u201358.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6018_CR8","doi-asserted-by":"crossref","unstructured":"Chen YC, Peng WC, Lee SY. Mining temporal patterns in interval-based data. In: 2016 IEEE 32nd international conference data engineering ICDE 2016. vol. 27(12) pp. 1506\u20137. 2016","DOI":"10.1109\/ICDE.2016.7498397"},{"issue":"4","key":"6018_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2508037.2508044","volume":"4","author":"I Batal","year":"2013","unstructured":"Batal I, Valizadegan H, Cooper GF, Hauskrecht M. A temporal pattern mining approach for classifying electronic health record data. ACM Trans Intell Syst Technol. 2013;4(4):1\u201322.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"6018_CR10","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/0933-3657(95)00036-4","volume":"8","author":"Y Shahar","year":"1996","unstructured":"Shahar Y, Musen MA. Knowledge-based temporal abstraction in clinical domains. Artif Intell Med. 1996;8:267\u201398.","journal-title":"Artif Intell Med"},{"key":"6018_CR11","doi-asserted-by":"crossref","unstructured":"Li J, Fu AWC, He H, Chen J, Jin H, McAullay D, et al. Mining risk patterns in medical data. In: proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. 2005;770\u20135.","DOI":"10.1145\/1081870.1081971"},{"issue":"8","key":"6018_CR12","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1017\/S1041610210001067","volume":"22","author":"PS Sachdev","year":"2010","unstructured":"Sachdev PS, Brodaty H, Reppermund S, Kochan N, Trollor JN, Draper B, et al. The Sydney memory and ageing study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70\u201390\u00a0years. Int Psychogeriatr. 2010;22(8):1248\u201364.","journal-title":"Int Psychogeriatr"},{"issue":"6","key":"6018_CR13","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3109\/09540261.2013.870137","volume":"25","author":"PS Sachdev","year":"2013","unstructured":"Sachdev PS, Lee T, Wen W, Ames D, Batouli AH, Bowden J, et al. The contribution of twins to the study of cognitive ageing and dementia: the Older Australian Twins Study. Int Rev Psychiatry. 2013;25(6):738\u201347. https:\/\/doi.org\/10.3109\/09540261.2013.870137.","journal-title":"Int Rev Psychiatry."},{"key":"6018_CR14","unstructured":"Alzheimer\u2019s Association. Facts and figures [Internet]. [cited 2021 Dec 10]. Available from: https:\/\/www.alz.org\/alzheimers-dementia\/facts-figures"},{"issue":"e201606210","key":"6018_CR15","first-page":"1","volume":"8","author":"DJ Selkoe","year":"2016","unstructured":"Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer\u2019s disease at 25 years. EMBO Mol Med. 2016;8(e201606210):1\u201314.","journal-title":"EMBO Mol Med."},{"issue":"2","key":"6018_CR16","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10115-009-0196-0","volume":"21","author":"P Papapetrou","year":"2009","unstructured":"Papapetrou P, Kollios G, Sclaroff S, Gunopulos D. Mining frequent arrangements of temporal intervals. Knowl Inf Syst. 2009;21(2):133\u201371.","journal-title":"Knowl Inf Syst"},{"issue":"11","key":"6018_CR17","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1145\/182.358434","volume":"26","author":"J Allen","year":"1983","unstructured":"Allen J. Maintaining knowledge about temporal intervals. Commun ACM. 1983;26(11):832\u201343.","journal-title":"Commun ACM"},{"key":"6018_CR18","doi-asserted-by":"crossref","unstructured":"Agrawal R, Srikant R. Mining sequential patterns. In: proceedings\u2014international conference on data engineering. 1995;3\u201314.","DOI":"10.1109\/ICDE.1995.380415"},{"key":"6018_CR19","unstructured":"Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, et al. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: proceedings of 17th international conference on data engineering. 2001;215\u201324."},{"issue":"1","key":"6018_CR20","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.datak.2006.10.009","volume":"63","author":"E Winarko","year":"2007","unstructured":"Winarko E, Roddick JF. ARMADA\u2014an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng. 2007;63(1):76\u201390.","journal-title":"Data Knowl Eng"},{"key":"6018_CR21","doi-asserted-by":"crossref","unstructured":"Chen YC, Jiang JC, Peng WC, Lee SY. An efficient algorithm for mining time interval-based patterns in large databases. In: proceedings of the 19th ACM international conference on Information and knowledge management. 2010;49\u201358.","DOI":"10.1145\/1871437.1871448"},{"key":"6018_CR22","doi-asserted-by":"crossref","unstructured":"Patel D, Hsu W, Lee ML. Mining relationships among interval-based events for classification. In: proceedings of the 2008 ACM SIGMOD international conference on management of data. 2008;393\u2013404.","DOI":"10.1145\/1376616.1376658"},{"issue":"1","key":"6018_CR23","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s10115-014-0784-5","volume":"45","author":"R Moskovitch","year":"2015","unstructured":"Moskovitch R, Shahar Y. Classification of multivariate time series via temporal abstraction and time intervals mining. Knowl Inf Syst. 2015;45(1):35\u201374. https:\/\/doi.org\/10.1007\/s10115-014-0784-5.","journal-title":"Knowl Inf Syst."},{"key":"6018_CR24","first-page":"452","volume":"2009","author":"R Moskovitch","year":"2009","unstructured":"Moskovitch R, Shahar Y. Medical temporal-knowledge discovery via temporal abstraction. AMIA Annu Symp Proc. 2009;2009:452\u20136.","journal-title":"AMIA Annu Symp Proc"},{"key":"6018_CR25","doi-asserted-by":"crossref","unstructured":"Batal I, Valizadegan H, Cooper G. A pattern mining approach for classifying multivariate temporal data. In: 2011 IEEE international conference on bioinformatics and biomedicine; 2011(November 12):358\u201365. Available from: http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=6120466","DOI":"10.1109\/BIBM.2011.39"},{"issue":"1","key":"6018_CR26","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10115-015-0819-6","volume":"46","author":"I Batal","year":"2016","unstructured":"Batal I, Cooper GF, Fradkin D, Harrison J, Moerchen F, Hauskrecht M. An efficient pattern mining approach for event detection in multivariate temporal data. Knowl Inf Syst. 2016;46(1):115\u201350.","journal-title":"Knowl Inf Syst"},{"issue":"5","key":"6018_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/exsy.12448","volume":"36","author":"A Kocheturov","year":"2019","unstructured":"Kocheturov A, Momcilovic P, Bihorac A, Pardalos PM. Extended vertical lists for temporal pattern mining from multivariate time series. Expert Syst. 2019;36(5):1\u201316.","journal-title":"Expert Syst"},{"key":"6018_CR28","doi-asserted-by":"crossref","unstructured":"Mantovani M, Combi C, Hauskrecht M. Mining compact predictive pattern sets using classification model. In: artificial intelligence in medicine: 17th conference on artificial intelligence in medicine, AIME 2019, Poznan, Poland, June 26\u201329, 2019, Proceedings; 2019:386\u201396.","DOI":"10.1007\/978-3-030-21642-9_49"},{"key":"6018_CR29","doi-asserted-by":"crossref","unstructured":"Mantovani M, Amico B, Combi C. Discovering predictive trend-event patterns in temporal clinical data. In: proceedings of the 36th Annual ACM Symposium on Applied Computing. 2021;570\u20139.","DOI":"10.1145\/3412841.3441937"},{"key":"6018_CR30","doi-asserted-by":"crossref","unstructured":"Khoshnevisan F, Ivy J, Capan M, Arnold R, Huddleston J, Chi M. Recent temporal pattern mining for septic shock early prediction. In: 2018 IEEE international conference on healthcare informatics (ICHI). 2018;229\u201340","DOI":"10.1109\/ICHI.2018.00033"},{"key":"6018_CR31","doi-asserted-by":"crossref","unstructured":"Orphanou K, Dagliati A, Sacchi L, Stassopoulou A, Keravnou E, Bellazzi R. Combining naive bayes classifiers with temporal association rules for coronary heart disease diagnosis. In: 2016 IEEE international conference on healthcare informatics (ICHI). 2016;81\u201392","DOI":"10.1109\/ICHI.2016.15"},{"issue":"3","key":"6018_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/14272","volume":"8","author":"MA Morid","year":"2020","unstructured":"Morid MA, Liu Sheng OR, Del Fiol G, Facelli JC, Bray BE, Abdelrahman S. Temporal pattern detection to predict adverse events in critical care: case study with acute kidney injury. JMIR Med Inform. 2020;8(3):1\u201329.","journal-title":"JMIR Med Inform"},{"key":"6018_CR33","unstructured":"Sheetrit E, Nissim N, Klimov D, Fuchs L, Elovici Y, Shahar Y. Temporal Pattern discovery for accurate sepsis diagnosis in ICU patients. 2017; Available from: http:\/\/arxiv.org\/abs\/1709.01720"},{"issue":"3","key":"6018_CR34","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/1132960.1132963","volume":"38","author":"L Geng","year":"2006","unstructured":"Geng L, Hamilton HJ. Interestingness measures for data mining: a survey. ACM Comput Surv. 2006;38(3):3.","journal-title":"ACM Comput Surv"},{"key":"6018_CR35","doi-asserted-by":"crossref","unstructured":"Li H, Li J, Wong L, Feng M, Tan YP. Relative risk and odds ratio: A data mining perspective. In: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. 2005;368\u201377.","DOI":"10.1145\/1065167.1065215"},{"issue":"1","key":"6018_CR36","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.artmed.2008.07.008","volume":"45","author":"J Li","year":"2009","unstructured":"Li J, Fu AWC, Fahey P. Efficient discovery of risk patterns in medical data. Artif Intell Med. 2009;45(1):77\u201389.","journal-title":"Artif Intell Med"},{"key":"6018_CR37","unstructured":"Tenny S, Hoffman MR. Relative risk. StatPearls [Internet] Treasure Isl StatPearls Publ [Internet]. 2021; Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK430824"},{"issue":"8","key":"6018_CR38","doi-asserted-by":"publisher","first-page":"e32319","DOI":"10.2196\/32319","volume":"10","author":"IJ Casanova","year":"2022","unstructured":"Casanova IJ, Campos M, Juarez JM, Gomariz A, Lorente-ros M. Using the diagnostic odds ratio to select patterns to build an interpretable pattern-based classifier in a clinical domain\u202f: multivariate sequential pattern mining study. JMIR Med Inform. 2022;10(8):e32319.","journal-title":"JMIR Med Inform"},{"issue":"3","key":"6018_CR39","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1136\/amiajnl-2012-000929","volume":"20","author":"DM Maslove","year":"2013","unstructured":"Maslove DM, Podchiyska T, Lowe HJ. Discretization of continuous features in clinical datasets. J Am Med Informatics Assoc. 2013;20(3):544\u201353.","journal-title":"J Am Med Informatics Assoc"},{"key":"6018_CR40","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin J, Keogh E, Wei L. Experiencing SAX\u202f: a novel symbolic representation of time series. Data Min Knowl Disc. 2007;15:107\u201344.","journal-title":"Data Min Knowl Disc"},{"issue":"1","key":"6018_CR41","doi-asserted-by":"publisher","first-page":"45","DOI":"10.7326\/0003-4819-132-1-200001040-00008","volume":"132","author":"Y Shahar","year":"2000","unstructured":"Shahar Y. Dimension of time in illness: an objective view. Ann Intern Med. 2000;132(1):45\u201353.","journal-title":"Ann Intern Med"},{"key":"6018_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-77220-w","author":"A Spooner","year":"2020","unstructured":"Spooner A, Sowmya A, Sachdev P, Kochan NA, Trollor J, Brodaty H. machine learning models for predicting dementia: a comparison of methods for survival analysis of high-dimensional clinical data. Nat Sci Rep. 2020. https:\/\/doi.org\/10.1038\/s41598-020-77220-w.","journal-title":"Nat Sci Rep"},{"issue":"18","key":"6018_CR43","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","volume":"247","author":"FE Harrell","year":"1982","unstructured":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA J Am Med Assoc. 1982;247(18):2543\u20136.","journal-title":"JAMA J Am Med Assoc"},{"issue":"10","key":"6018_CR44","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno H, Cai T, Pencina MJ, D\u2019Agostino RB, Wei LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30(10):1105\u201317.","journal-title":"Stat Med"},{"issue":"478","key":"6018_CR45","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1198\/016214507000000149","volume":"102","author":"H Uno","year":"2007","unstructured":"Uno H, Cai T, Tian L, Wei LJ. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc. 2007;102(478):527\u201337.","journal-title":"J Am Stat Assoc"},{"key":"6018_CR46","unstructured":"The Python language reference [Internet]. Available from: https:\/\/docs.python.org\/3\/reference\/"},{"key":"6018_CR47","doi-asserted-by":"publisher","unstructured":"Katana. https:\/\/doi.org\/10.26190\/669X-A286. 2010.","DOI":"10.26190\/669X-A286"},{"issue":"10248","key":"6018_CR48","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/S0140-6736(20)30367-6","volume":"396","author":"G Livingston","year":"2020","unstructured":"Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413\u201346.","journal-title":"Lancet"},{"key":"6018_CR49","doi-asserted-by":"publisher","unstructured":"J\u00f8rgensen LB, Thorleifsson BM, Selb\u00e6k G, \u0160altyte Benth J, Helvik AS. Physical diagnoses in nursing home residents - Is dementia or severity of dementia of importance? BMC Geriatr. 2018;18(1):1-14. https:\/\/doi.org\/10.1186\/s12877-018-0943-8.","DOI":"10.1186\/s12877-018-0943-8"},{"key":"6018_CR50","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.dadm.2017.09.001","volume":"9","author":"SL Risacher","year":"2017","unstructured":"Risacher SL, Tallman EF, West JD, Yoder KK, Hutchins GD, Fletcher JW, et al. Olfactory identification in subjective cognitive decline and mild cognitive impairment: association with tau but not amyloid positron emission tomography. Alzheimer\u2019s Dement Diagn Assess Dis Monit. 2017;9:57\u201366. https:\/\/doi.org\/10.1016\/j.dadm.2017.09.001.","journal-title":"Alzheimer\u2019s Dement Diagn Assess Dis Monit"},{"issue":"3","key":"6018_CR51","doi-asserted-by":"publisher","first-page":"749","DOI":"10.3233\/JAD-2010-091561","volume":"20","author":"TC de Toledo Ferraz Alves","year":"2010","unstructured":"de Toledo Ferraz Alves TC, Ferreira LK, Wajngarten M, Busatto GF. Cardiac disorders as risk factors for Alzheimer\u2019s disease. J Alzheimer\u2019s Dis. 2010;20(3):749\u201363.","journal-title":"J Alzheimer\u2019s Dis"},{"key":"6018_CR52","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10654-017-0225-3","volume":"32","author":"W Xu","year":"2017","unstructured":"Xu W, Wang H, Wan Y, et al. Alcohol consumption and dementia risk: a dose\u2013response meta-analysis of prospective studies. Eur J Epidemiol. 2017;32:21\u201342.","journal-title":"Eur J Epidemiol"},{"issue":"January","key":"6018_CR53","first-page":"1","volume":"7","author":"C Sierra","year":"2020","unstructured":"Sierra C. Hypertension and the risk of dementia. Front Cardiovasc Med. 2020;7(January):1\u20137.","journal-title":"Front Cardiovasc Med"},{"issue":"6","key":"6018_CR54","doi-asserted-by":"publisher","first-page":"e65841","DOI":"10.1371\/journal.pone.0065841","volume":"8","author":"DM Lipnicki","year":"2013","unstructured":"Lipnicki DM, Sachdev PS, Crawford J, Reppermund S, Kochan NA, Trollor JN, et al. Risk factors for late-life cognitive decline and variation with age and sex in the Sydney memory and ageing study. PLoS One. 2013;8(6):e65841.","journal-title":"PLoS One."},{"issue":"June","key":"6018_CR55","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.jad.2019.08.041","volume":"259","author":"K Nakamura","year":"2019","unstructured":"Nakamura K, Watanabe Y, Kitamura K, Kabasawa K, Someya T. Psychological distress as a risk factor for dementia after the 2004 Niigata-Chuetsu earthquake in Japan. J Affect Disord. 2019;259(June):121\u20137. https:\/\/doi.org\/10.1016\/j.jad.2019.08.041.","journal-title":"J Affect Disord"},{"issue":"3","key":"6018_CR56","first-page":"290","volume":"97","author":"N Mesbah","year":"2017","unstructured":"Mesbah N, Perry M, Hill KD, Kaur M, Hale L. Postural stability in older adults with alzheimer disease. Phys Ther. 2017;97(3):290\u2013309.","journal-title":"Phys Ther"},{"issue":"2","key":"6018_CR57","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1017\/S1041610292000991","volume":"4","author":"TH Crook","year":"1992","unstructured":"Crook TH, Feher EP, Larrabee GJ. Assessment of memory complaint in age- associated memory impairment: the MAC-Q. Int Psychogeriatr. 1992;4(2):165\u201376.","journal-title":"Int Psychogeriatr"},{"issue":"7404","key":"6018_CR58","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1136\/bmj.326.7404.1418-h","volume":"326","author":"G Scott","year":"2003","unstructured":"Scott G. Mental activity may help prevent dementia. BMJ. 2003;326(7404):1418. https:\/\/doi.org\/10.1136\/bmj.326.7404.1418-h.","journal-title":"BMJ"},{"issue":"3","key":"6018_CR59","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1002\/alz.13523","volume":"20","author":"ED Beck","year":"2024","unstructured":"Beck ED, Yoneda T, James BD, Bennett DA, Hassenstab J, Katz MJ, et al. Personality predictors of dementia diagnosis and neuropathological burden: an individual participant data meta-analysis. Alzheimer\u2019s Dement. 2024;20(3):1497\u2013514.","journal-title":"Alzheimer\u2019s Dement"},{"issue":"2","key":"6018_CR60","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1001\/archpsyc.64.2.234","volume":"64","author":"RS Wilson","year":"2007","unstructured":"Wilson RS, Krueger KR, Arnold SE, Schneider JA, Kelly JF, Barnes LL, et al. Loneliness and risk of Alzheimer disease. Arch Gen Psychiatry. 2007;64(2):234\u201340.","journal-title":"Arch Gen Psychiatry"},{"issue":"9","key":"6018_CR61","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1093\/gerona\/glx261","volume":"73","author":"JE Lee","year":"2018","unstructured":"Lee JE, Shin DW, Jeong SM, Son KY, Cho B, Yoon JL, et al. Association between timed up and go test and future dementia onset. J Gerontol Ser A Biol Sci Med Sci. 2018;73(9):1238\u201343.","journal-title":"J Gerontol Ser A Biol Sci Med Sci"},{"issue":"7","key":"6018_CR62","doi-asserted-by":"publisher","first-page":"e2424539","DOI":"10.1001\/jamanetworkopen.2024.24539","volume":"7","author":"EL Ferguson","year":"2024","unstructured":"Ferguson EL, Thoma M, Buto PT, Wang J, Glymour MM, Hoffmann TJ, et al. Visual impairment, eye conditions, and diagnoses of neurodegeneration and dementia. JAMA Netw Open. 2024;7(7):e2424539.","journal-title":"JAMA Netw Open"},{"issue":"9","key":"6018_CR63","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1111\/jgs.15456","volume":"66","author":"HR Davies-Kershaw","year":"2018","unstructured":"Davies-Kershaw HR, Hackett RA, Cadar D, Herbert A, Orrell M, Steptoe A. Vision impairment and risk of dementia: findings from the english longitudinal study of ageing. J Am Geriatr Soc. 2018;66(9):1823\u20139.","journal-title":"J Am Geriatr Soc"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-06018-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-06018-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-06018-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T02:03:04Z","timestamp":1739844184000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-06018-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6018"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-06018-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"12 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","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 Human Research Ethics Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service granted ethics approval for the MAS study and written consent was given by all participants and informants. The MAS study and this work were carried out in accordance with the MAS Governance guidelines, which are based on relevant University of New South Wales and National Health and Medical Research Council research and ethics policies. The OATS study was approved by the ethics committees of the Australian Twin Registry, University of New South Wales, University of Melbourne, Queensland Institute of Medical Research and the South Eastern Sydney & Illawarra Area Health Service.","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 that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"56"}}