S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test
Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. Methodology: In this study, we...
Saved in:
| Published in: | IEEE journal of biomedical and health informatics Vol. 28; no. 8; pp. 4456 - 4470 |
|---|---|
| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.08.2024
|
| Subjects: | |
| ISSN: | 2168-2194, 2168-2208, 2168-2208 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. Methodology: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. Results: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. Conclusions: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff. |
|---|---|
| AbstractList | Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices.BACKGROUNDComputer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices.In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community.METHODOLOGYIn this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community.The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance.RESULTSThe experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance.In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.CONCLUSIONSIn conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff. Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. Methodology: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. Results: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. Conclusions: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff. Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff. |
| Author | Zhou, Tian Wang, Haoran Li, Yunxia Zhang, Xiaochen Zhang, Wei Li, Yanping Zang, Yaning Tang, Shiyu Li, Renren Yuan, Xiao Tang, Zeshen |
| Author_xml | – sequence: 1 givenname: Zeshen orcidid: 0000-0001-8765-6464 surname: Tang fullname: Tang, Zeshen email: 2011610@tongji.edu.cn organization: College of Electronic and Information Engineering, Tongji University, Shanghai, China – sequence: 2 givenname: Shiyu surname: Tang fullname: Tang, Shiyu email: tangshiyuzoe@163.com organization: Naval Medical Center, Naval Medical University, Shanghai, China – sequence: 3 givenname: Haoran orcidid: 0000-0002-4622-0119 surname: Wang fullname: Wang, Haoran email: 1910664@tongji.edu.cn organization: College of Electronic and Information Engineering, Tongji University, Shanghai, China – sequence: 4 givenname: Renren orcidid: 0000-0003-3781-9848 surname: Li fullname: Li, Renren email: 1453097lee@tongji.edu.cn organization: Department of Neurology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China – sequence: 5 givenname: Xiaochen surname: Zhang fullname: Zhang, Xiaochen email: zxc_9803@163.com organization: Department of Neurology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China – sequence: 6 givenname: Wei orcidid: 0000-0003-4970-2073 surname: Zhang fullname: Zhang, Wei email: zhangweihb@hotmail.com organization: Department of Neuropsychology, Zhongnan Hospital of Wuhan University, Wuhan, China – sequence: 7 givenname: Xiao surname: Yuan fullname: Yuan, Xiao email: 1043269491@qq.com organization: Department of Neurology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China – sequence: 8 givenname: Yaning surname: Zang fullname: Zang, Yaning email: zangyn1015@163.com organization: Chengdu Second People's Hospital, Sichuan, China – sequence: 9 givenname: Yanping surname: Li fullname: Li, Yanping email: 2011611@tongji.edu.cn organization: College of Electronic and Information Engineering, Tongji University, Shanghai, China – sequence: 10 givenname: Tian orcidid: 0009-0005-6773-6308 surname: Zhou fullname: Zhou, Tian email: 1941716@tongji.edu.cn organization: College of Electronic and Information Engineering, Tongji University, Shanghai, China – sequence: 11 givenname: Yunxia orcidid: 0000-0002-0626-2584 surname: Li fullname: Li, Yunxia email: doctorliyunxia@163.com organization: Department of Neurology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38819974$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU9v1DAQxS3UipbSD4CEkI9csvhPnNjcttVCi1pV1a72Gk2dMTIk8WInlfj2ON0tQhzqy9gzv-eR3ntDjoYwICHvOFtwzsynbxdX1wvBRLmQJau15q_IqeCVLoRg-uj5zk15Qs5T-sHy0bllqtfkRGbcmLo8Jbu12N4X2-XqM11j74v1tMP46BO2dIt2DJHeTzCMc6PYQvQw-jBAR5fTGFaDDS1G6jI1v_s8tHT1CN30hNHg6CaC7-gt_PTDd7rBNL4lxw66hOeHekY2X1aby6vi5u7r9eXyprBSiLFQXCvtajRcCrBgHHA0Gkpg1ipTSedQWW1AKcAHqWQramdqBi1UrVGtPCMf99_uYvg15b1N75PFroMBw5QaySpZKmN4ndEPB3R66LFtdtH3EH83zyZlgO8BG0NKEd1fhLNmzqKZs2jmLJpDFllT_6exfnxyZZwdeVH5fq_0iPjPJlVKVhr5B_RFln0 |
| CODEN | IJBHA9 |
| CitedBy_id | crossref_primary_10_1142_S021812662550433X crossref_primary_10_1177_13872877251343241 |
| Cites_doi | 10.2466/pms.1958.8.3.271 10.1371/journal.pone.0089078 10.1016/j.future.2021.09.032 10.1145/3196959.3196972 10.1109/taffc.2023.3285997 10.1145/233269.233324 10.2307/3212444 10.1080/13854040590947498 10.1037/h0044509 10.12659/msm.889776 10.1111/j.1467-8659.1990.tb00398.x 10.2196/42637 10.30773/pi.2018.07.22 10.1136/gpsych-2022-100751 10.1007/978-3-642-12098-5_3 10.1111/ggi.12014 10.1016/j.knosys.2021.107338 10.1109/JBHI.2014.2299793 10.1109/ICIEV.2017.8338607 10.3389/fnhum.2017.00496 10.1016/j.future.2023.05.012 10.1016/j.ins.2019.07.026 10.1109/TPAMI.2013.50 10.3791/60456-v 10.1002/1097-4679(198707)43:4<402::AID-JCLP2270430411>3.0.CO;2-E 10.1002/int.22957 10.5555/3001460.3001507 10.1145/1066157.1066213 10.1076/jcen.20.2.280.1161 10.1007/978-3-030-32281-6_3 10.1093/geroni/igy038 10.1609/aaai.v33i01.33015885 10.1109/JBHI.2023.3240508 10.1016/j.knosys.2022.109323 10.1016/j.jalz.2011.03.003 10.1016/j.aei.2021.101317 10.1609/aaai.v33i01.33013894 10.1109/TFUZZ.2020.2979150 10.1145/1247480.1247546 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1109/JBHI.2024.3407881 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2168-2208 |
| EndPage | 4470 |
| ExternalDocumentID | 38819974 10_1109_JBHI_2024_3407881 10543049 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China; National Science Foundation of China grantid: 81671307 funderid: 10.13039/501100001809 – fundername: STI2030-Major Projects grantid: 2022ZD0211600 – fundername: Shanghai Municipal Health Commission grantid: 2022JC018 funderid: 10.13039/100017950 – fundername: Shanghai Committee of Science and Technology grantid: 22Y11903500 – fundername: Shanghai Hospital Development Center Foundation grantid: SHDC12021110 |
| GroupedDBID | 0R~ 4.4 6IF 6IH 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7X8 |
| ID | FETCH-LOGICAL-c322t-51858f7e9132aca9fa1e98a4a0cc5963ffe5c89a55aeb353d27f970ada6d95d3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001301988500040&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2194 2168-2208 |
| IngestDate | Sun Sep 28 00:57:56 EDT 2025 Thu Apr 03 07:03:43 EDT 2025 Sat Nov 29 04:18:37 EST 2025 Tue Nov 18 21:39:25 EST 2025 Wed Aug 27 02:35:22 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c322t-51858f7e9132aca9fa1e98a4a0cc5963ffe5c89a55aeb353d27f970ada6d95d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-8765-6464 0000-0002-4622-0119 0000-0003-3781-9848 0009-0005-6773-6308 0000-0002-0626-2584 0000-0003-4970-2073 |
| PMID | 38819974 |
| PQID | 3063459917 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | crossref_primary_10_1109_JBHI_2024_3407881 proquest_miscellaneous_3063459917 ieee_primary_10543049 pubmed_primary_38819974 crossref_citationtrail_10_1109_JBHI_2024_3407881 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-01 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE journal of biomedical and health informatics |
| PublicationTitleAbbrev | JBHI |
| PublicationTitleAlternate | IEEE J Biomed Health Inform |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 Kingma (ref40) 2014 ref15 ref14 Lundberg (ref50) 2017 Kingma (ref36) 2014; 1050 ref52 ref11 ref10 Kim (ref44) 2018 ref17 ref16 ref19 ref18 Paige (ref46) 2017 ref51 Lopez (ref45) 2018 Maaten (ref49) 2008; 9 ref48 ref47 ref41 ref43 Ankerst (ref33) 1999 ref8 ref7 Dupont (ref38) 2018 ref9 ref4 ref3 ref6 Gauthier (ref2) 2021 ref5 ref35 ref34 ref31 ref30 ref32 ref1 Chen (ref37) 2018 Oord (ref39) 2017 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 Higgins (ref42) 2017 |
| References_xml | – ident: ref4 doi: 10.2466/pms.1958.8.3.271 – ident: ref52 doi: 10.1371/journal.pone.0089078 – ident: ref19 doi: 10.1016/j.future.2021.09.032 – ident: ref35 doi: 10.1145/3196959.3196972 – ident: ref17 doi: 10.1109/taffc.2023.3285997 – ident: ref32 doi: 10.1145/233269.233324 – volume: 9 start-page: 2579 issue: 11 year: 2008 ident: ref49 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – ident: ref27 doi: 10.2307/3212444 – ident: ref26 doi: 10.1080/13854040590947498 – start-page: 4766 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2017 ident: ref50 article-title: A unified approach to interpreting model predictions – ident: ref3 doi: 10.1037/h0044509 – ident: ref6 doi: 10.12659/msm.889776 – ident: ref29 doi: 10.1111/j.1467-8659.1990.tb00398.x – ident: ref22 doi: 10.2196/42637 – ident: ref7 doi: 10.30773/pi.2018.07.22 – ident: ref1 doi: 10.1136/gpsych-2022-100751 – start-page: 2610 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2018 ident: ref37 article-title: Isolating sources of disentanglement in variational autoencoders – ident: ref34 doi: 10.1007/978-3-642-12098-5_3 – ident: ref9 doi: 10.1111/ggi.12014 – ident: ref20 doi: 10.1016/j.knosys.2021.107338 – ident: ref11 doi: 10.1109/JBHI.2014.2299793 – ident: ref51 doi: 10.1109/ICIEV.2017.8338607 – ident: ref10 doi: 10.3389/fnhum.2017.00496 – volume: 1050 year: 2014 ident: ref36 article-title: Auto-encoding variational bayes publication-title: Stat – ident: ref21 doi: 10.1016/j.future.2023.05.012 – ident: ref13 doi: 10.1016/j.ins.2019.07.026 – ident: ref41 doi: 10.1109/TPAMI.2013.50 – ident: ref8 doi: 10.3791/60456-v – ident: ref24 doi: 10.1002/1097-4679(198707)43:4<402::AID-JCLP2270430411>3.0.CO;2-E – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. year: 2017 ident: ref42 article-title: $\beta$-VAE: Learning basic visual concepts with a constrained variational framework – ident: ref23 doi: 10.1002/int.22957 – ident: ref31 doi: 10.5555/3001460.3001507 – start-page: 5926 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2017 ident: ref46 article-title: Learning disentangled representations with semi-supervised deep generative models – ident: ref28 doi: 10.1145/1066157.1066213 – start-page: 6114 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2018 ident: ref45 article-title: Information constraints on auto-encoding variational bayes – ident: ref25 doi: 10.1076/jcen.20.2.280.1161 – ident: ref14 doi: 10.1007/978-3-030-32281-6_3 – ident: ref12 doi: 10.1093/geroni/igy038 – ident: ref43 doi: 10.1609/aaai.v33i01.33015885 – ident: ref5 doi: 10.1109/JBHI.2023.3240508 – start-page: 2649 volume-title: Proc. Int. Conf. Mach. Learn. year: 2018 ident: ref44 article-title: Disentangling by factorising – start-page: 3581 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2014 ident: ref40 article-title: Semi-supervised learning with deep generative models – ident: ref16 doi: 10.1016/j.knosys.2022.109323 – ident: ref47 doi: 10.1016/j.jalz.2011.03.003 – ident: ref18 doi: 10.1016/j.aei.2021.101317 – ident: ref48 doi: 10.1609/aaai.v33i01.33013894 – start-page: 49 volume-title: Proc. ACM SIGMOD Int. Conf. Manag. Data Rec. year: 1999 ident: ref33 article-title: Optics: Ordering points to identify the clustering structure – start-page: 19 year: 2021 ident: ref2 article-title: World alzheimer report 2021: Journey through the diagnosis of dementia – ident: ref15 doi: 10.1109/TFUZZ.2020.2979150 – start-page: 6307 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2017 ident: ref39 article-title: Neural discrete representation learning – ident: ref30 doi: 10.1145/1247480.1247546 – start-page: 710 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2018 ident: ref38 article-title: Learning disentangled joint continuous and discrete representations |
| SSID | ssj0000816896 |
| Score | 2.4309416 |
| Snippet | Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective,... Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 4456 |
| SubjectTerms | Aged Aged, 80 and over Algorithms Cognitive Dysfunction - diagnosis Cognitive impairment computer-aided diagnosis Correlation Deep Learning Diagnosis, Computer-Assisted - methods Feature extraction Female Hospitals Humans Male Neurology Older adults semi-supervised Signal Processing, Computer-Assisted Supervised Machine Learning Task analysis Trail Making Test trail making test (TMT) Trajectory variational autoencoder (VAE) vector quantization (VQ) |
| Title | S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test |
| URI | https://ieeexplore.ieee.org/document/10543049 https://www.ncbi.nlm.nih.gov/pubmed/38819974 https://www.proquest.com/docview/3063459917 |
| Volume | 28 |
| WOSCitedRecordID | wos001301988500040&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2168-2208 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816896 issn: 2168-2194 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD6oiPji_TJvRPBJiPaSNI1vUyYqKMrG2FtJkxQEbcdc_f2epN3QBwWfmoYkbfPlNOeS5AM40yHXzHGEqZAZyowInKMppVwGcR6KnKMB58kmxNNTOhrJ53azut8LY631i8_shUv6WL6pdO1cZSjhnLmw0CIsCpE0m7XmDhXPIOH5uCJMUJRE1kYxw0BePlzf3aM1GLGL2EWuUscQE-NFSsF-TEmeY-V3ddNPO7fr_3zhDVhr9UvSbQbEJizYcgtWHtsI-jaM-9HwhQ67vSvSt--vtF-P3d_iwxoy9A588lJjZ7sMOkQ7uvUVkm49rXql2wA_Iajn-nt_2ivpzc8LJ1VBcO57fSOPnuOKDPDLdmBw2xvc3NGWdYFqFO4p5TiDp4WwEu1UpZUsVGhlqpgKtOYorkVhuU6l4lwhjjw2kSikCJRRiZHcxLuwVFal3QdiE6k0NmbCwDKV6zyKMEvYRCU6R7W0A8Gs3zPdnkjuiDHeMm-ZBDJzqGUOtaxFrQPn8yrj5jiOvwrvOEi-FWzQ6MDpDN0MhclFSFRpq_ojQ_spZhxVZtGBvQb2ee3ZaDn4pdVDWHUPbxYHHsHSdFLbY1jWnwjZ5ARH7Cg98SP2Cxjf5AM |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9sgFD7asmnry65dm3UXJu1pEi22IZi-pVOiZEuiRYmivlkYsFSptaM03u_fATvR-pBJezJGgDAfx5wL8AF8NZEw3HOE6Yhbyq1k3tGUUqFYkkcyF2jABbIJOZul19fqV3tYPZyFcc6FzWfu3CdDLN9WpvauMpRwwX1Y6DE8EZzHrDmutXepBA6JwMgVY4KiLPI2jhkxdfHjajRGezDm54mPXaWeIybBh1KSP1iUAsvKYYUzLDzDl__Z5VfwotUwSb-ZEq_hkSvfwLNpG0N_C-tFvJrTVX9wSRbu7oYu6rX_X9w7S1bBhU_mNQ63z6ArtKRbbyHp19tqUPoj8BuCmm54D_e9ksH-xnBSFQRXv5tbMg0sV2SJX3YMy-Fg-X1EW94FalC8t1TgGp4W0im0VLXRqtCRU6nmmhkjUGCLwgmTKi2ERiRFYmNZKMm01T2rhE3eQaesSncKxPWUNtiYjZjjOjd5HGOWdD3dMzkqpl1gu3HPTHsnuafGuM2CbcJU5lHLPGpZi1oXvu2rrJsLOf5V-NhD8lfBBo0ufNmhm6E4-RiJLl1V32doQSVcoNIsu3DSwL6vvZst7w-0-hmej5bTSTYZz36ewZHvSLNV8AN0tpvafYSn5jfCt_kU5u0fu8TmYg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=S2VQ-VAE%3A+Semi-Supervised+Vector+Quantised-Variational+AutoEncoder+for+Automatic+Evaluation+of+Trail+Making+Test&rft.jtitle=IEEE+journal+of+biomedical+and+health+informatics&rft.au=Tang%2C+Zeshen&rft.au=Tang%2C+Shiyu&rft.au=Wang%2C+Haoran&rft.au=Li%2C+Renren&rft.date=2024-08-01&rft.eissn=2168-2208&rft.volume=28&rft.issue=8&rft.spage=4456&rft_id=info:doi/10.1109%2FJBHI.2024.3407881&rft_id=info%3Apmid%2F38819974&rft.externalDocID=38819974 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2194&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2194&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2194&client=summon |