ArticleNPJ digital medicine2022
Data-driven causal model discovery and personalized prediction in Alzheimer's disease.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Data-driven modeling of spatiotemporal dynamics using multimodal imaging data.PLoS computational biology · 2026Article
- Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.NPJ systems biology and applications · 2026Article
- Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions.Computer methods in applied mechanics and engineering · 2026Article
- Article
- ZENN: A thermodynamics-inspired computational framework for heterogeneous data-driven modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction-Diffusion Dynamics.Journal of computational physics · 2025Article
- PatientProfiler: building patient-specific signaling models from proteogenomic data.Molecular systems biology · 2025Article
- Article
- DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.Scientific reports · 2025Article
- Can artificial intelligence uncover the bioactive peptides' benefits for human health and knowledge? A narrative review.Frontiers in nutrition · 2025Review
- Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms.Translational psychiatry · 2024Review
- Article
- Data-driven care for patients with neurodegenerative disorders.Nature reviews. Neurology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.