mlsanityData to 14 Oct 2026

Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis

arXiv 2205.01188 ↗First posted 2 May 2022cs.LGcited by 5

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Abstract (arXiv)

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors may yield an accurate estimate of time-to-conversion to DAT for patients at various disease stages. We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival analysis model that extends the classic Cox regression model to predict time-to-conversion to DAT. Our findings showed that genetic features contributed the least to survival analysis, while CDC features contributed the most. 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. Consequently, our study demonstrated that using the current clinical procedure, which includes gathering cognitive test results, can outperform survival analysis results produced using costly genetic or CSF data.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

10 of 63 references.

References · 10

  1. 2015Cyclical Learning Rates for Training Neural Networkscited by 629
  2. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
  3. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  4. 2016Entity Embeddings of Categorical Variablescited by 87
  5. 2018A Scalable Discrete-Time Survival Model for Neural Networkscited by 72
  6. 2019Continuous and Discrete-Time Survival Prediction with Neural Networkscited by 70
  7. 2019A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Datacited by 12
  8. 2022Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Diseasecited by 3
  9. 2017Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imagingcited by 2
  10. 2020An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Groupcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2023Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohortcited by 1
  2. 2024HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progressioncited by 1
  3. 2024Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingcited by 0
  4. 2024Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modellingcited by 0
  5. 2026iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysiscited by 0