{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:52:03Z","timestamp":1761745923163},"reference-count":42,"publisher":"International Research and Cooperation Association for Bio & Socio-Sciences Advancement (IRCA-BSSA)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BST"],"published-print":{"date-parts":[[2024,2,29]]},"DOI":"10.5582\/bst.2024.01026","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T22:12:33Z","timestamp":1708380753000},"page":"66-72","source":"Crossref","is-referenced-by-count":3,"title":["Predictive deep learning models for cognitive risk using accessible data"],"prefix":"10.5582","volume":"18","author":[{"given":"Kenji","family":"Karako","sequence":"first","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3542","reference":[{"key":"1","unstructured":"1. Cabinet Office, 2022 Edition of the White Paper on an Aging Society: Chapter 1 The State of Aging (Section 1, 2) <i>https:\/\/www8.cao.go.jp\/kourei\/whitepaper\/w-2022\/html\/zenbun\/s1_1_2.html<\/i> (accessed February 10, 2024) (in Japanese)"},{"key":"2","unstructured":"2. Cabinet Office, 2022 Edition of the White Paper on an Aging Society: Chapter 1 The State of Aging (Section 1, 1) <i>https:\/\/www8.cao.go.jp\/kourei\/whitepaper\/w-2022\/html\/zenbun\/s1_1_1.html<\/i> (accessed February 10, 2024) (in Japanese)"},{"key":"3","unstructured":"3. World Health Organization, Dementia: A public health priority. World Health Organization <i>https:\/\/www.who.int\/publications\/i\/item\/dementia-a-public-health-priority<\/i> (accessed February 10, 2024)"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989; 1: 541-551.","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Folstein MF, Folstein SE, McHugh PR. &quot;Mini-mental state&quot; A practical method for grading the cognitive state of patients for clinicians. J Psychiatr Res. 1975; 12: 189-98.","DOI":"10.1016\/0022-3956(75)90026-6"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Dumurgier J, Hanseeuw BJ, Hatling FB, Judge KA, Schultz AP, Chhatwal JP, Blacker D, Sperling RA, Johnson KA, Hyman BT, G\u00f3mez-Isla T. Alzheimer&apos;s disease biomarkers and future decline in cognitive normal older adults. J. Alzheimer&apos;s Dis. 2017; 60:1451-1459.","DOI":"10.3233\/JAD-170511"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun. ACM. 2017; 60:84-90.","DOI":"10.1145\/3065386"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016:770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"9","unstructured":"9. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014; https:\/\/doi.org\/10.48550\/arXiv.1409.1556"},{"key":"10","unstructured":"10. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. 2015; https:\/\/doi.org\/10.48550\/arXiv.1506.01497"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016:779-788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Lee C, Chau H, Wang H, Chuang Y, Chau Y. Detection of mild cognitive impairment by facial videos. IEEE Int. Conf. Consum. Electron.-Taiwan. 2022:197-198.","DOI":"10.1109\/ICCE-Taiwan55306.2022.9869203"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Tufekcioglu Z, Bilgic B, Zeylan AE, Salah AA, Dibeklioglu H, Emre M. Do Alzheimer&apos;s disease patients appear younger than their real age? Dement Geriatr Cogn Disord. 2020; 49:483-488.","DOI":"10.1159\/000510359"},{"key":"14","unstructured":"14. Lai WS, Huang JB, Yang MH. Semi-supervised learning for optical flow with generative adversarial networks. Adv. Neural Inf. Process. Syst. 2017; 30."},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Sun J, Dodge HH, Mahoor MH. MC-ViViT: Multi-branch classifier-ViViT to detect mild cognitive impairment in older adults using facial videos. 2023; https:\/\/doi.org\/10.48550\/arXiv.2304.05292","DOI":"10.2139\/ssrn.4485802"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Yang Q, Li X, Ding X, Xu F, Ling Z. Deep learning-based speech analysis for Alzheimer&apos;s disease detection: A literature review. Alzheimers Res Ther. 2022; 14:186-186.","DOI":"10.1186\/s13195-022-01131-3"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Croisile B, Brabant M, Carmoi T, Lepage Y, Aimard G, Trillet M. Comparison between oral and written spelling in Alzheimer&apos;s disease. Brain Lang. 1996; 54:361-387.","DOI":"10.1006\/brln.1996.0081"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Croisile B, Ska B, Brabant M, Duchene A, Lepage Y, Aimard G, Trillet M. Comparative study of oral and written picture description in patients with Alzheimer&apos;s disease. Brain Lang. 1996; 53:1-19.","DOI":"10.1006\/brln.1996.0033"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Cuetos F, Arango-Lasprilla JC, Uribe C, Valencia C, Lopera F. Linguistic changes in verbal expression: A preclinical marker of Alzheimer&apos;s disease. J Int Neuropsychol Soc. 2007; 13:433-439.","DOI":"10.1017\/S1355617707070609"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Rohanian M, Hough J, Purver M. Alzheimer&apos;s dementia recognition using acoustic, lexical, disfluency and speech pause features robust to noisy inputs. 2021; https:\/\/doi.org\/10.48550\/arXiv.2106.15684","DOI":"10.21437\/Interspeech.2021-1633"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9:1735-1780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Medsker L, Jain LC. Recurrent neural networks: Design and applications. CRC press. 1999.","DOI":"10.1201\/9781420049176"},{"key":"23","unstructured":"23. Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. 2018; https:\/\/doi.org\/10.48550\/arXiv.1810.04805"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Liu Z, Proctor L, Collier PN, Zhao X. Automatic diagnosis and prediction of cognitive decline associated with Alzheimer&apos;s dementia through spontaneous speech. IEEE Int. Conf. Signal Image Processing Appl. 2021:39-43.","DOI":"10.1109\/ICSIPA52582.2021.9576784"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Lopes M, Brucki SMD, Giampaoli V, Mansur LL. Semantic verbal fluency test in dementia: Preliminary retrospective analysis. Dement Neuropsychol. 2009; 3:315-20.","DOI":"10.1590\/S1980-57642009DN30400009"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Campagna F, Montagnese S, Ridola L, Senzolo M, Schiff S, De Rui M, et al. The animal naming test: An easy tool for the assessment of hepatic encephalopathy. Hepatology. 2017; 66:198-208.","DOI":"10.1002\/hep.29146"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Chien YW, Hong SY, Cheah WT, Fu LC, Chang YL. An assessment system for Alzheimer&apos;s disease based on speech using a novel feature sequence design and recurrent neural network. IEEE Int. Conf. Syst. Man Cybern. 2018; 3289-3294.","DOI":"10.1109\/SMC.2018.00557"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Illes J. Neurolinguistic features of spontaneous language production dissociate three forms of neurodegenerative disease: Alzheimer&apos;s, Huntington&apos;s, and Parkinson&apos;s. Brain Lang. 1989; 37:628-642.","DOI":"10.1016\/0093-934X(89)90116-8"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Becker JT, Boller F, Lopez OL, Saxton J, McGonigle KL. The natural history of Alzheimer&apos;s disease: Description of study cohort and accuracy of diagnosis. Arch. Neurol. 1994; 51:585-594.","DOI":"10.1001\/archneur.1994.00540180063015"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. Luz S, Haider F, de la Fuente S, Fromm D, MacWhinney B.Alzheimer&apos;s dementia recognition through spontaneous speech: The ADReSS challenge. 2020; https:\/\/doi.org\/10.48550\/arXiv.2004.06833","DOI":"10.21437\/Interspeech.2020-2571"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Sakatani K, Oyama K, Hu L. Deep learning-based screening test for cognitive impairment using basic blood test data for health examination. Front. Neurol. 2020; 11:588140.","DOI":"10.3389\/fneur.2020.588140"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Van Der Flier WM, Skoog I, Schneider JA, Pantoni L, Mok V, Chen CL, Scheltens P. Vascular cognitive impairment. Nat. Rev. Dis. Primers. 2018; 4:1-16.","DOI":"10.1038\/nrdp.2018.3"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Al-Qazzaz NK, Ali SH, Ahmad SA, Islam S, Mohamad K. Cognitive impairment and memory dysfunction after a stroke diagnosis: A post-stroke memory assessment. Neuropsychiatr Dis Treat. 2014; 1677-1691.","DOI":"10.2147\/NDT.S67184"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Gorelick PB, Scuteri A, Black SE, <i>et al<\/i>. Vascular contributions to cognitive impairment and dementia: A statement for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke. 2011; 42:2672-2713.","DOI":"10.1161\/STR.0b013e3182299496"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Brooke J, Ojo O Enteral nutrition in dementia: A systematic review. Nutrients. 2015; 7:2456-2468.","DOI":"10.3390\/nu7042456"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Hong CH, Falvey C, Harris TB, Simonsick EM, Satterfield S, Ferrucci L, Metti AL, Patel KV, Yaffe K. Anemia and risk of dementia in older adults: Findings from the Health ABC study. Neurology. 2013; 81:528-533.","DOI":"10.1212\/WNL.0b013e31829e701d"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Qizilbash N, Gregson J, Johnson ME, Pearce N, Douglas I, Wing K, Evans SJW, Pocock SJ. BMI and risk of dementia in two million people over two decades: A retrospective cohort study. Lancet Diabetes Endocrinol. 2015; 3:431-436.","DOI":"10.1016\/S2213-8587(15)00033-9"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. Delgado-Alvarado M, Gago B, Navalpotro-Gomez I, Jim\u00e9nez-Urbieta H, Rodriguez-Oroz MC. Biomarkers for dementia and mild cognitive impairment in Parkinson&apos;s disease. Mov. Disord. 2016; 31:861-881.","DOI":"10.1002\/mds.26662"},{"key":"39","doi-asserted-by":"crossref","unstructured":"39. Miranda AS, Cordeiro TM, dos Santos Lacerda Soares TM, Ferreira RN, Simoes e Silva AC. Kidney\u2013brain axis inflammatory cross-talk: from bench to bedside. Clin. Sci. 2017; 131:1093-1105.","DOI":"10.1042\/CS20160927"},{"key":"40","doi-asserted-by":"crossref","unstructured":"40. Lee H, Shahzad A, Kim K. Automated prescreening of MCI through deep learning models based on wearable inertial sensors data. Alzheimers. Dement. 2021; 17: e052744.","DOI":"10.1002\/alz.052744"},{"key":"41","doi-asserted-by":"crossref","unstructured":"41. Shahzad A, Dadlani A, Lee H, Kim K. Automated prescreening of mild cognitive impairment using shank-mounted inertial sensors based gait biomarkers. IEEE Access. 2022; 10:15835-15844.","DOI":"10.1109\/ACCESS.2022.3149100"},{"key":"42","doi-asserted-by":"crossref","unstructured":"42. Burns A, Greene BR, McGrath MJ, O&apos;Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V. SHIMMER\u2122 A wireless sensor platform for noninvasive biomedical research. IEEE Sens. J. 2010; 10:1527-1534.","DOI":"10.1109\/JSEN.2010.2045498"}],"container-title":["BioScience Trends"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/bst\/18\/1\/18_2024.01026\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T04:14:19Z","timestamp":1711167259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/bst\/18\/1\/18_2024.01026\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.5582\/bst.2024.01026","relation":{},"ISSN":["1881-7815","1881-7823"],"issn-type":[{"value":"1881-7815","type":"print"},{"value":"1881-7823","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,29]]},"article-number":"2024.01026"}}