Mirabnahrazam, Ghazal, Ma, Da, Beaulac, Cédric, Lee, Sieun, Popuri, Karteek, Lee, Hyunwoo, Cao, Jiguo, Galvin, James E, Wang, Lei, Beg, Mirza Faisal, Initiative, the Alzheimer's Disease Neuroimaging · May 2, 2022
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
Markdown · JSON · Original source
The paper’s authors report a model for estimating -to-conversion to Dementia of Alzheimer's Type (DAT), using data from 401 subjects in the Alzheimer's Disease Neuroimaging Initiative. They combined 63 features from MRI, genetic, and CDC data modalities; CDC here means cognitive tests, demographic information, and CSF data. Their findings rank CDC features as contributing most to the survival analysis and genetic features least. Combining MRI and genetic features improved survival prediction over either modality alone, but adding CDC to other combinations performed only as well as using CDC features alone. The authors say this means that, using the current clinical procedure, gathering cognitive test results can outperform results produced using costly genetic or CSF data. This is a finding from the study, not evidence that the model predicts an individual patient’s course reliably in clinical practice. The abstract gives no performance figures or details of validation, so the size and broader applicability of the reported differences remain unclear.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
q-bio.NC · Article
Source details
Original source date: May 2, 2022 (2022-05-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T20:17:17.769Z. First observed: 2026-11-11T05:01:25.696Z. Retrieved: 2026-11-11T20:16:36.670Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The evidence provided is the paper’s abstract; it gives no performance figures for the reported predictions. The abstract does not provide details of validation, limiting what can be concluded here about how broadly the findings apply.
Open question
How much did combining MRI and genetic features improve survival prediction over using either modality alone?
Source-stated
The authors used 401 subjects and 63 features drawn from MRI, genetic, and CDC data modalities to predict time-to-conversion to DAT.
We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities
Source-stated
The authors report that CDC features contributed most to the survival analysis and genetic features contributed least.
genetic features contributed the least to survival analysis, while CDC features contributed the most.
Source-stated
The authors report that combining MRI and genetic features improved survival prediction over either modality alone; adding CDC to a combination performed as well as using CDC features alone.
Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features.
Source-stated
The authors conclude that, using the current clinical procedure, cognitive test results can outperform survival analysis results produced using costly genetic or CSF data.
using the current clinical procedure, which includes gathering cognitive test results, can outperform survival analysis results produced using costly genetic or CSF data.
Source and contributor context
Mirabnahrazam, Ghazal, Ma, Da, Beaulac, Cédric, Lee, Sieun, Popuri, Karteek, Lee, Hyunwoo, Cao, Jiguo, Galvin, James E, Wang, Lei, Beg, Mirza Faisal, Initiative, the Alzheimer's Disease Neuroimaging · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: f7c6f654-cdfe-4099-8e80-efc9a909b29c.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T20:17:17.769Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 6229a1b4a7c48b50216404c214680ef416851dfa64de0aaf48d69ba91c1e2031. Source text hash: 2be3b8f5747f55cadc8d00bc895f44ba5441e36010d19c0d8ae2826598687132.
Revision and source-check history
Version 1 · 2026-11-11T20:17:17.769Z
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
The paper’s authors report a model for estimating -to-conversion to Dementia of Alzheimer's Type (DAT), using data from 401 subjects in the Alzheimer's Disease Neuroimaging Initiative. They combined 63 features from MRI, genetic, and CDC data modalities; CDC here means cognitive tests, demographic information, and CSF data. Their findings rank CDC features as contributing most to the survival analysis and genetic features least. Combining MRI and genetic features improved survival prediction over either modality alone, but adding CDC to other combinations performed only as well as using CDC features alone. The authors say this means that, using the current clinical procedure, gathering cognitive test results can outperform results produced using costly genetic or CSF data. This is a finding from the study, not evidence that the model predicts an individual patient’s course reliably in clinical practice. The abstract gives no performance figures or details of validation, so the size and broader applicability of the reported differences remain unclear.
Original source
q-bio.NC · Article
Source details
Original source date: May 2, 2022 (2022-05-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T20:17:17.769Z. First observed: 2026-11-11T05:01:25.696Z. Retrieved: 2026-11-11T20:16:36.670Z.
Limits
The evidence provided is the paper’s abstract; it gives no performance figures for the reported predictions. The abstract does not provide details of validation, limiting what can be concluded here about how broadly the findings apply.
Open question
How much did combining MRI and genetic features improve survival prediction over using either modality alone?
Source-stated
The authors used 401 subjects and 63 features drawn from MRI, genetic, and CDC data modalities to predict time-to-conversion to DAT.
We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities
Source-stated
The authors report that CDC features contributed most to the survival analysis and genetic features contributed least.
genetic features contributed the least to survival analysis, while CDC features contributed the most.
Source-stated
The authors report that combining MRI and genetic features improved survival prediction over either modality alone; adding CDC to a combination performed as well as using CDC features alone.
Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features.
Source-stated
The authors conclude that, using the current clinical procedure, cognitive test results can outperform survival analysis results produced using costly genetic or CSF data.
using the current clinical procedure, which includes gathering cognitive test results, can outperform survival analysis results produced using costly genetic or CSF data.
Source and contributor context
Mirabnahrazam, Ghazal, Ma, Da, Beaulac, Cédric, Lee, Sieun, Popuri, Karteek, Lee, Hyunwoo, Cao, Jiguo, Galvin, James E, Wang, Lei, Beg, Mirza Faisal, Initiative, the Alzheimer's Disease Neuroimaging · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: f7c6f654-cdfe-4099-8e80-efc9a909b29c.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T20:17:17.769Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 6229a1b4a7c48b50216404c214680ef416851dfa64de0aaf48d69ba91c1e2031. Source text hash: 2be3b8f5747f55cadc8d00bc895f44ba5441e36010d19c0d8ae2826598687132.
uncertain · 2026-11-11T20:17:17.769Z · anthropic/claude-opus-5.5
Publication: f7c6f654-cdfe-4099-8e80-efc9a909b29c
attribution — pass: The findings and conclusion are consistently attributed to the paper's authors. The data source, the Alzheimer's Disease Neuroimaging Initiative, is named correctly. The 'current clinical procedure' conclusion is framed as the authors' own claim.
Consequently, our study demonstrated that using the current clinical procedure
summary — uncertain: The summary calls the method only 'a model'. It drops the abstract's central AI element: a deep learning-based survival analysis model that extends classic Cox regression. That element is why this belongs in an AI stream. Several central terms also lack plain explanation for non-specialists: 'time-to-conversion' (time until progression to dementia), 'survival analysis', and CSF (cerebrospinal fluid). Smallest repair: name the deep learning extension of Cox regression and gloss these terms or flag them for tooltips. The question is a fair open issue.
We used a deep learning-based survival analysis model that extends the classic Cox regression model to predict time-to-conversion to DAT.
date — pass: The original publication date of 2022-05-02 matches the v1 arXiv submission. The 2026 feed date is correctly not used.
[Submitted on 2 May 2022]
support — pass: Every claim matches the abstract's wording. The caveats about the missing performance figures and validation details are accurate and appropriately conditional. No benefits or costs are invented beyond the authors' cost comparison.
adding CDC to any combination of features only worked as well as using only CDC features
media — pass: The selected media is the verified direct link to the original arXiv abstract page. This is the most direct access to the source.
Challenge or correct this reading
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TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv