PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
- URL: http://arxiv.org/abs/2604.28055v1
- Date: Thu, 30 Apr 2026 16:01:30 GMT
- Title: PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
- Abstract summary: Alzheimer's disease (AD) progression prediction requires models that use irregular visits, account for avoid diagnostic leakage, and provide horizon risks.<n>We propose PROMISE-AD, a leakage-safe survival framework for predicting conversion from cognitively normal (CN) to mild cognitive impairment (MCI)<n>ProMISE-AD converts pre-index visits into MCI tokens with standardized measurements, missingness masks, longitudinal changes, time-normalized slopes, visit timing, and non-diagnostic categorical attributes.
- Score: 12.168319671275375
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Individualized Alzheimer's disease (AD) progression prediction requires models that use irregular visits, account for censoring, avoid diagnostic leakage, and provide calibrated horizon risks. We propose PROgression-aware MultI-horizon Survival Estimation for Alzheimer's Disease (PROMISE-AD), a leakage-safe survival framework for predicting conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to AD dementia using ADNI/TADPOLE tabular histories. PROMISE-AD converts pre-index visits into tokens with standardized measurements, missingness masks, longitudinal changes, time-normalized slopes, visit timing, and non-diagnostic categorical attributes. A temporal Transformer fuses global, attention-pooled, and latest-visit representations to estimate a progression score and latent discrete-time mixture hazards. Training combines survival likelihood, horizon-specific focal risk loss, progression ranking, hazard smoothness, and mixture-balance regularization, followed by validation-set isotonic calibration for 1-, 2-, 3-, and 5-year risks. In held-out testing across three seeds, PROMISE-AD achieved an integrated Brier score (IBS) of 0.085 $\pm$ 0.012, C-index of 0.808 $\pm$ 0.015, and mean time-dependent AUC of 0.840 $\pm$ 0.081 for CN-to-MCI conversion, yielding the lowest IBS among compared methods. For MCI-to-AD conversion, PROMISE-AD achieved the highest C-index (0.894 $\pm$ 0.018) and near-ceiling 5-year discrimination (AUROC 0.997 $\pm$ 0.003; AUPRC 0.999 $\pm$ 0.001), although some baselines had lower IBS. Ablations and interpretability supported longitudinal change features, fused temporal representations, mixture hazards, cognitive and functional measures, APOE4 status, and recent conversion-proximal visits. These findings suggest that progression-aware survival modeling can provide interpretable multi-horizon AD conversion risk estimates.
Related papers
- Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees [0.0]
Predicting conversion from mild cognitive impairment to Alzheimer's disease is central to trial enrichment and care planning.<n>Existing models provide no individual-level uncertainty estimates and rarely include transparent leakage audits.<n>We introduce the first application of persistent homology to longitudinal clinical trajectory point clouds for this task.
arXiv Detail & Related papers (2026-07-20T00:00:17Z) - Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning [10.269961654607108]
We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and uncertainty estimation.<n>On ADNI, the model outperforms linear, recurrent, and transformer baselines for next-visit diagnosis prediction.
arXiv Detail & Related papers (2026-06-23T14:04:27Z) - Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories [2.3948878478085938]
We develop an anchor-based analysis of 24-month Clinical Dementia Rating Sum of Boxes (CDR-SB) change.<n>We propose a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning.
arXiv Detail & Related papers (2026-05-04T23:05:28Z) - Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort [0.0]
Predicting whether someone with mild cognitive impairment will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration.<n>The Boundary Sharpness Coefficient measures how well-defined the gray-white matter boundary looks on structural MRI.<n>This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects.
arXiv Detail & Related papers (2026-03-27T01:53:59Z) - Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
arXiv Detail & Related papers (2026-02-27T04:49:01Z) - Dual Model Deep Learning for Alzheimer Prognostication [4.970364068620607]
PROGRESS transforms a single baseline cerebrospinal fluid biomarker assessment into actionable prognostic estimates.<n>A deep survival model estimates time to conversion from mild cognitive impairment to dementia.<n>It substantially outperforms Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction.
arXiv Detail & Related papers (2025-12-22T07:08:20Z) - Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution [42.85462513661566]
We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay.<n>A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes.<n>On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model while maintaining well-calibrated predictions.
arXiv Detail & Related papers (2025-11-19T20:11:49Z) - Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers [0.9509915687694127]
Traditional survival models fail to capture complex longitudinal biomarker patterns associated with disease progression.<n>We propose an ensemble survival analysis framework integrating multiple survival models to improve early prediction of clinical progression.<n>One follow-up visit after baseline significantly improved prediction accuracy (48.1% C-index, 48.2% AUC gains)
arXiv Detail & Related papers (2025-03-20T18:56:32Z) - Comprehensive Methodology for Sample Augmentation in EEG Biomarker Studies for Alzheimers Risk Classification [0.0]
Alzheimer's disease (AD), the leading type, accounts for 70% of cases.<n>EEG measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging.<n>This study integrates signal processing, harmonization, and statistical techniques to enhance sample size and improve AD risk classification reliability.
arXiv Detail & Related papers (2024-11-20T10:31:02Z) - AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions.
Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations.
With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
arXiv Detail & Related papers (2024-07-02T16:44:00Z) - Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes [44.974100402600165]
We study the evaluation of a policy best-parametric and worst-case perturbations to a decision process (MDP)
We use transition observations from the original MDP, whether they are generated under the same or a different policy.
Our estimator is also estimated statistical inference using Wald confidence intervals.
arXiv Detail & Related papers (2024-03-29T18:11:49Z) - AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive
Care Units [20.96242356493069]
We propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different disease states in health trajectory.
Our model shows a path towards identifying patients at risk so that health practitioners can intervene early and save patient lives.
arXiv Detail & Related papers (2021-02-09T08:44:31Z) - COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital
Contact Tracing [68.68882022019272]
COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns.
We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
arXiv Detail & Related papers (2020-10-30T00:47:01Z) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.