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Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model

Abstract:
Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia\u27s underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/s12916-022-02250-2

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-7531-4459
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Role:
Author
ORCID:
0000-0002-8094-0902
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-6813-8493
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0003-3555-9181
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-9431-258X


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Funder identifier:
https://ror.org/03x94j517
Grant:
MC-PC-17215
More from this funder
Funder identifier:
10.13039/501100000272
Grant:
RP-2017-08-ST2-006
More from this funder
Funder identifier:
10.13039/501100013373
Grant:
BRC-1215-20005
More from this funder
Funder identifier:
10.13039/501100000266
Grant:
EP/N026977/1


Publisher:
BioMed Central
Journal:
BMC Medicine More from this journal
Volume:
20
Issue:
1
Pages:
45-45
Article number:
45
Publication date:
2022-02-01
DOI:
EISSN:
1741-7015
ISSN:
1741-7015


Language:
English
Keywords:
Pubs id:
1237261
Local pid:
pubs:1237261
Source identifiers:
W4210765044
Deposit date:
2026-04-09
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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