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
Source Excerpt
arXiv: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. …
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