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

arXiv: https://arxiv.org/abs/2205.01188 · HTML: https://mlsanity.com/paper/2205.01188/

## 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 (most cited first, 10 of 10)

- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data](https://mlsanity.com/paper/1909.03890/) (2019)
- [Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease](https://mlsanity.com/paper/2203.05707/) (2022)
- [Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging](https://mlsanity.com/paper/1711.00671/) (2017)
- [An 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 Group](https://mlsanity.com/paper/2001.05534/) (2020)

## Built on by (most cited first, 5 of 5)

- [Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort](https://mlsanity.com/paper/2306.10326/) (2023)
- [HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression](https://mlsanity.com/paper/2404.03208/) (2024)
- [Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling](https://mlsanity.com/paper/2405.08780/) (2024)
- [Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling](https://mlsanity.com/paper/2409.16231/) (2024)
- [iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis](https://mlsanity.com/paper/2607.08778/) (2026)

## 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.