{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:38:07Z","timestamp":1775083087505,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031430749","type":"print"},{"value":"9783031430756","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43075-6_34","type":"book-chapter","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T07:02:17Z","timestamp":1694502137000},"page":"395-406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Multimodal Approaches for\u00a0Alzheimer\u2019s Detection Using Patients\u2019 Speech and\u00a0Transcript"],"prefix":"10.1007","author":[{"given":"Hongmin","family":"Cai","sequence":"first","affiliation":[]},{"given":"Xiaoke","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhengliang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wenxiong","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Haixing","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Zihao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Dajiang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","unstructured":"Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.J., Srivastava, M., Chang, K.W.: Generating natural language adversarial examples. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2890\u20132896. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-1316","DOI":"10.18653\/v1\/D18-1316"},{"key":"34_CR2","doi-asserted-by":"publisher","unstructured":"Ao, J., et al.: SpeechT5: unified-modal encoder-decoder pre-training for spoken language processing (2022). https:\/\/doi.org\/10.48550\/arXiv.2110.07205, arxiv.org\/abs\/2110.07205","DOI":"10.48550\/arXiv.2110.07205"},{"issue":"6","key":"34_CR3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1001\/archneur.1994.00540180063015","volume":"51","author":"JT Becker","year":"1994","unstructured":"Becker, J.T., Boller, F., Lopez, O.L., Saxton, J., McGonigle, K.L.: The natural history of Alzheimer\u2019s disease: description of study cohort and accuracy of diagnosis. Archiv. Neurol. 51(6), 585\u2013594 (1994). https:\/\/doi.org\/10.1001\/archneur.1994.00540180063015","journal-title":"Archiv. Neurol."},{"key":"34_CR4","doi-asserted-by":"publisher","unstructured":"Ben Ammar, R., Ben Ayed, Y.: Speech processing for early Alzheimer disease diagnosis: machine learning based approach. In: 2018 IEEE\/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1\u20138 (2018). https:\/\/doi.org\/10.1109\/AICCSA.2018.8612831, iSSN: 2161\u20135330","DOI":"10.1109\/AICCSA.2018.8612831"},{"key":"34_CR5","doi-asserted-by":"publisher","unstructured":"Bertini, F., Allevi, D., Lutero, G., Calz\u00e0, L., Montesi, D.: An automatic Alzheimer\u2019s disease classifier based on spontaneous spoken English. Comput. Speech Lang. 72, 101298 (2022). https:\/\/doi.org\/10.1016\/j.csl.2021.101298, www.sciencedirect.com\/science\/article\/pii\/S0885230821000991","DOI":"10.1016\/j.csl.2021.101298"},{"key":"34_CR6","doi-asserted-by":"publisher","unstructured":"Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 740\u2013750. Association for Computational Linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1082, www.aclanthology.org\/D14-1082","DOI":"10.3115\/v1\/D14-1082"},{"key":"34_CR7","doi-asserted-by":"publisher","unstructured":"Chen, S., et al.: WavLM: Large-scale self-supervised pre-training for full stack speech processing. IEEE J. Sel. Top. Sign. Process. 16(6), 1505\u20131518 (2022). https:\/\/doi.org\/10.1109\/JSTSP.2022.3188113, arxiv.org\/abs\/2110.13900","DOI":"10.1109\/JSTSP.2022.3188113"},{"key":"34_CR8","unstructured":"Chen, Y., Wu, L., Zaki, M.: Iterative deep graph learning for graph neural networks: better and robust node embeddings. In: Advances in Neural Information Processing Systems, vol. 33, pp. 19314\u201319326. Curran Associates, Inc. (2020). www.proceedings.neurips.cc\/paper\/2020\/hash\/e05c7ba4e087beea9410929698dc41a6-Abstract.html"},{"key":"34_CR9","doi-asserted-by":"publisher","unstructured":"Dai, H., et al.: AugGPT: leveraging ChatGPT for text data augmentation (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.13007, http:\/\/arxiv.org\/abs\/2302.13007, arXiv:2302.13007 [cs]","DOI":"10.48550\/arXiv.2302.13007"},{"key":"34_CR10","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1613\/jair.4992","volume":"57","author":"Y Goldberg","year":"2016","unstructured":"Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345\u2013420 (2016)","journal-title":"J. Artif. Intell. Res."},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Guo, J., Qiu, W., Li, X., Zhao, X., Guo, N., Li, Q.: Predicting Alzheimer\u2019s disease by hierarchical graph convolution from positron emission tomography imaging. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5359\u20135363. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005971"},{"key":"34_CR13","doi-asserted-by":"publisher","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2018). https:\/\/doi.org\/10.48550\/arXiv.1706.02216, http:\/\/arxiv.org\/abs\/1706.02216, arXiv:1706.02216 [cs, stat]","DOI":"10.48550\/arXiv.1706.02216"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Jing, B., Xie, P., Xing, E.: On the automatic generation of medical imaging reports. arXiv preprint arXiv:1711.08195 (2017)","DOI":"10.18653\/v1\/P18-1240"},{"key":"34_CR15","doi-asserted-by":"publisher","unstructured":"Li, D., et al.: Contextualized perturbation for textual adversarial attack (2021). https:\/\/doi.org\/10.48550\/arXiv.2009.07502, arxiv.org\/abs\/2009.07502","DOI":"10.48550\/arXiv.2009.07502"},{"key":"34_CR16","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks (2017). www.arxiv.org\/abs\/1511.05493, arXiv:1511.05493 [cs, stat]"},{"key":"34_CR17","doi-asserted-by":"publisher","unstructured":"Liu, N., Luo, K., Yuan, Z., Chen, Y.: A transfer learning method for detecting alzheimer\u2019s disease based on speech and natural language processing. Front. Public Health 10, 772592 (2022). https:\/\/doi.org\/10.3389\/fpubh.2022.772592, www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9043451\/","DOI":"10.3389\/fpubh.2022.772592"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Martinc, M., Haider, F., Pollak, S., Luz, S.: Temporal integration of text transcripts and acoustic features for Alzheimer\u2019s diagnosis based on spontaneous speech. Front. Aging Neurosci. 13, 642647 (2021). www.frontiersin.org\/articles\/10.3389\/fnagi.2021.642647","DOI":"10.3389\/fnagi.2021.642647"},{"issue":"11","key":"34_CR19","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"34_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-3-031-21014-3_28","volume-title":"Machine Learning in Medical Imaging, MLMI 2022","author":"S Rezayi","year":"2022","unstructured":"Rezayi, S., et al.: ClinicalRadioBERT: knowledge-infused few shot learning for clinical notes named entity recognition. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) Machine Learning in Medical Imaging, MLMI 2022. Lecture Notes in Computer Science, vol. 13583, pp. 269\u2013278. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21014-3_28"},{"issue":"1","key":"34_CR21","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s12911-021-01456-3","volume":"21","author":"A Roshanzamir","year":"2021","unstructured":"Roshanzamir, A., Aghajan, H., Soleymani Baghshah, M.: Transformer-based deep neural network language models for Alzheimer\u2019s disease risk assessment from targeted speech. BMC Med. Inform. Decis. Making 21(1), 92 (2021). https:\/\/doi.org\/10.1186\/s12911-021-01456-3","journal-title":"BMC Med. Inform. Decis. Making"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Tschannen, M., Mustafa, B., Houlsby, N.: CLIPPO: image-and-language understanding from pixels only (2023). arxiv.org\/abs\/2212.08045","DOI":"10.1109\/CVPR52729.2023.01059"},{"key":"34_CR23","doi-asserted-by":"publisher","unstructured":"Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions (2017). https:\/\/doi.org\/10.48550\/arXiv.1708.05123, arxiv.org\/abs\/1708.05123","DOI":"10.48550\/arXiv.1708.05123"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W.Y., Yang, D.: That\u2019s so annoying!!!: a lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2557\u20132563 (2015)","DOI":"10.18653\/v1\/D15-1306"},{"key":"34_CR25","doi-asserted-by":"publisher","unstructured":"Wu, L., et al.: Graph neural networks for natural language processing: a survey (2021). https:\/\/doi.org\/10.48550\/arXiv.2106.06090, www.arxiv.org\/abs\/2106.06090, arXiv:2106.06090 [cs]","DOI":"10.48550\/arXiv.2106.06090"},{"key":"34_CR26","doi-asserted-by":"publisher","unstructured":"Yamanki, S.C., Sebasti\u00e1n, S.C., Jacobo, P.G.W., Humberto, G.A., Sa\u00fal, T.A.: Semantic feature extraction using SBERT for dementia detection. Brain sciences 12(2) (2022). https:\/\/doi.org\/10.3390\/brainsci12020270, www.pubmed.ncbi.nlm.nih.gov\/35204032\/, publisher: Brain Sci","DOI":"10.3390\/brainsci12020270"},{"key":"34_CR27","doi-asserted-by":"publisher","first-page":"654381","DOI":"10.3389\/fnhum.2021.654381","volume":"15","author":"P You","year":"2021","unstructured":"You, P., Li, X., Wang, Z., Wang, H., Dong, B., Li, Q.: Characterization of brain iron deposition pattern and its association with genetic risk factor in Alzheimer\u2019s disease using susceptibility-weighted imaging. Front. Hum. Neurosci. 15, 654381 (2021)","journal-title":"Front. Hum. Neurosci."},{"key":"34_CR28","doi-asserted-by":"publisher","first-page":"102082","DOI":"10.1016\/j.media.2021.102082","volume":"72","author":"L Zhang","year":"2021","unstructured":"Zhang, L., et al.: Deep fusion of brain structure-function in mild cognitive impairment. Med. Image Anal. 72, 102082 (2021)","journal-title":"Med. Image Anal."},{"key":"34_CR29","doi-asserted-by":"publisher","first-page":"102463","DOI":"10.1016\/j.media.2022.102463","volume":"79","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Wang, L., Zhu, D., Initiative, A.D.N., et al.: Predicting brain structural network using functional connectivity. Med. Image Anal. 79, 102463 (2022)","journal-title":"Med. Image Anal."},{"key":"34_CR30","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43075-6_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T23:01:28Z","timestamp":1730070088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43075-6_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031430749","9783031430756"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43075-6_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hoboken, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wi-consortium.org\/conferences\/bi2023\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CyberChair System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}