Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

• URL: http://arxiv.org/abs/2606.11794v1
• Date: Wed, 10 Jun 2026 08:26:11 GMT
• Title: Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data
• Abstract summary: Alzheimer's disease (AD) requires accurate and scalable tools for assessing disease severity.<n>We propose an attention-enhanced machine learning framework with ordinal regression for automated and interpretable AD severity staging.
• Score: 0.9786690381850356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical staging remains time-intensive and prone to variability. We propose an attention-enhanced multimodal machine learning framework with ordinal regression for automated and interpretable AD severity staging. The framework integrates T1-weighted MRI with demographic and genetic variables and compares unimodal and multimodal architectures using ordinal and non-ordinal prediction heads. Models were trained and validated using cohort-stratified splits derived from the ADNI, AIBL, and NIFD datasets. A strictly held-out test set was constructed using subjects excluded from all training, validation, preprocessing, and hyperparameter tuning procedures, with subject-level splitting employed throughout to prevent data leakage. Among unimodal approaches, the T1-weighted MRI model achieved slightly higher adjacent-stage accuracy (0.963) and agreement with clinical staging (QWK 0.444) than the tabular model (QWK 0.433). Integrating imaging, demographic, and genetic information improved overall performance. The multimodal non-ordinal baseline achieved the lowest prediction error (MAE 0.340), whereas the ordinal multimodal model achieved the highest adjacent-stage accuracy (0.970) and strongest agreement with clinical staging (QWK 0.549). These findings indicate that ordinal formulations better capture the ordered structure of the CDR scale and yield predictions more consistent with clinical staging. Explainability analyses using Grad CAM++ and SHAP demonstrated anatomically and clinically plausible model behavior, supporting transparent decision-making. Overall, attention-based multimodal learning with ordinal regression represents a robust, interpretable, and scalable approach for automated AD severity staging and AI-assisted clinical decision support.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Multimodal Fusion of Histopathology Images and Electronic Health Records for Early Breast Cancer Diagnosis [15.70912899013153]
  This paper presents a systematic framework that integrates patch-level histopathology features with structured clinical data.<n>We train and evaluate unimodal image models (a simple CNN baseline and ResNet-18 with transfer learning) and intermediate-fusion models (XGBoost and a multilayer perceptron)<n>Our results demonstrate that multimodal integration delivers meaningful improvements in both predictive performance and clinical transparency.
  arXiv  Detail & Related papers  (2026-04-18T19:46:34Z)
• Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment [56.62016795093786]
  Cerebra is an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.<n>It supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete.<n>Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation.
  arXiv  Detail & Related papers  (2026-03-23T05:46:45Z)
• MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis [2.2399170518036913]
  Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management.<n>Recent studies have increasingly focused on computer-aided diagnostic models to enhance precision and reliability.<n>To overcome these limitations, the Meta-Relational Copula-Based Graph Attention Network (MRC-GAT) is proposed as an efficient multimodal model for AD classification tasks.
  arXiv  Detail & Related papers  (2026-02-17T17:15:32Z)
• Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics [3.0991186209192794]
  We introduce a novel data-driven alternative based on Latent Ordinary Differential Equations (Latent ODEs) for tacrolimus AUC prediction.<n>This deep learning approach learns individualized dynamics directly from sparse clinical data.<n>Latent ODE model demonstrated superior robustness, maintaining high accuracy even when underlying biological mechanisms deviated from standard assumptions.
  arXiv  Detail & Related papers  (2026-02-03T07:30:48Z)
• 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)
• Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation [1.7061463565692456]
  Ocular-induced abnormal head posture (AHP) is a compensatory mechanism that arises from ocular misalignment.<n>This study addresses both challenges through two complementary deep learning frameworks.<n>AHP-CADNet is a multi-level attention fusion framework for automated diagnosis.<n> curriculum learning-based imputation framework is designed to mitigate missing data.
  arXiv  Detail & Related papers  (2025-10-07T07:51:59Z)
• Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach [7.212939068975618]
  We utilized data about 10,326 CKD patients, combining their clinical and claims information from 2009 to 2018.<n>A 24-month observation window was identified as optimal for balancing early detection and prediction accuracy.<n>The 2021 eGFR equation improved prediction accuracy and reduced racial bias, notably for African American patients.
  arXiv  Detail & Related papers  (2024-10-02T03:21:01Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Ensemble model for pre-discharge icd10 coding prediction [45.82374977939355]
  We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:02:56Z)

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.