# Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

arXiv: https://arxiv.org/abs/1903.07173 · HTML: https://mlsanity.com/paper/1903.07173/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progression](https://mlsanity.com/paper/2310.03353/) (2023)
- [Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes](https://mlsanity.com/paper/2206.12414/) (2022)
- [CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data](https://mlsanity.com/paper/2104.03739/) (2021)
- [Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI](https://mlsanity.com/paper/2304.04673/) (2023)
- [Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning](https://mlsanity.com/paper/2606.24604/) (2026)
- [Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment](https://mlsanity.com/paper/2206.08019/) (2022)
- [Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI](https://mlsanity.com/paper/2212.12810/) (2022)
- [Spatio-Temporal Similarity Measure based Multi-Task Learning for Predicting Alzheimer's Disease Progression using MRI Data](https://mlsanity.com/paper/2311.03557/) (2023)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field](https://mlsanity.com/paper/2406.17822/) (2024)
- [Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression](https://mlsanity.com/paper/2510.10433/) (2025)
- [DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning](https://mlsanity.com/paper/2608.25073/) (2026)

## Abstract (arXiv)

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p < 0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals.