The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fusing Heterogeneous Data for Alzheimer's Disease Classification
Open accessAbstract
In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic accuracy and how the methods compare against each other for this problem. Our results indicate that multimodal data fusion improves classification accuracy.
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“Nearly all these papers performed numericalization of image features in essence converting them to structured data prior to processing, however, two performed matrix factorization34,36.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [36]
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Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.05.004 · Cited by 249
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Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250
Metadata
- Authors
- Sudhir, P. P., Tze-Yun, L., & Initiative, A. D. N.
- Affiliations
- National University of Singapore (SG)
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2015
- Volume
- 216
- Pages
- 731-5
- ISSN
- 0926-9630 (ISSN-L)
- OpenAlex
- W1134109422
- Cited by
- 13
- References
- 1
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 2 · 2018: 1 · 2017: 1
- FWCI
- 4.16
- Type
- book chapter
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Studies in health technology and informatics (publishedVersion)
- PubMed (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- PubMed
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, multimodal fusion, multi-view learning, neuroimaging, cerebrospinal fluid biomarkers, dementia diagnosis, ADNI database, classification accuracy
- MeSH terms
- Machine Learning, Alzheimer Disease, Diagnosis, Computer-Assisted, Humans, Medical Record Linkage, Sensitivity and Specificity, Reproducibility of Results, Biomarkers, Decision Support Systems, Clinical, Data Mining, Electronic Health Records, Neuroimaging
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