Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
Quick Answer
This paper shows that The GNOVA framework combines a GRU encoder and Neural ODE decoder to predict Alzheimer's disease trajectories using routine data, achieving mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without neuroimaging.
Quick Take
This model utilizes data from 1,727 patients over 10 years, enabling clinicians to make informed prognostic decisions in resource-constrained settings.
Key Points
- GNOVA integrates GRU and Neural ODE for Alzheimer's trajectory forecasting.
- Achieved mean absolute errors of 1.35 (CDR-SB) and 2.28 (MMSE) scores.
- Utilizes routine visit data, avoiding costly neuroimaging methods.
- Analyzed data from 1,727 patients over a decade.
- Strong predictors include age, BMI, and APOE4 status.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 07798v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings.
In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits.
We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point.
The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1. 35 and 2. 28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.
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