Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring

• URL: http://arxiv.org/abs/2507.09460v1
• Date: Sun, 13 Jul 2025 02:56:40 GMT
• Title: Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring
• Authors: Noah Marchal, William E. Janes, Mihail Popescu, Xing Song,
• Abstract summary: Self-attention achieved the lowest prediction error for subscale-level models.<n>We identified distinct homogeneity-hegeneity profiles across functional domains.
• Score: 3.210027230758067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a case series cohort by targeting ALSFRS- R scale trajectories with continuous in-home sensor monitoring data. Our analysis compared three model paradigms (individual batch learning and cohort-level batch versus incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results revealed cohort homogeneity across functional domains responding to learning methods, with transfer learning improving prediction error for ALSFRS-R subscales in 28 of 32 contrasts (mean RMSE=0.20(0.04)), and individual batch learning for predicting the composite scale (mean RMSE=3.15(1.25)) in 2 of 3. Self-attention interpolation achieved the lowest prediction error for subscale-level models (mean RMSE=0.19(0.06)), capturing complex nonlinear progression patterns, outperforming linear and cubic interpolations in 20 of 32 contrasts, though linear interpolation proved more stable in all ALSFRS-R composite scale models (mean RMSE=0.23(0.10)). We identified distinct homogeneity-heterogeneity profiles across functional domains with respiratory and speech exhibiting patient-specific patterns benefiting from personalized incremental adaptation, while swallowing and dressing functions followed cohort-level trajectories suitable for transfer models. These findings suggest that matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in ALS progression tracking. Integrating adaptive model selection within sensor monitoring platforms could enable timely interventions and scalable deployment in future multi-center studies.

Related papers

• Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction [13.937881108738042]
  We propose a diffusion-based framework that synthesizes clinically plausible future sMRI representations directly from baseline data.<n>Experiments on ADNI and AIBL cohorts show that MCI-Diff outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-06-05T07:01:05Z)
• Interpretable Deep Regression Models with Interval-Censored Failure Time Data [1.2993568435938014]
  Deep learning methods for interval-censored data remain underexplored and limited to specific data type or model.<n>This work proposes a general regression framework for interval-censored data with a broad class of partially linear transformation models.<n>Applying our method to the Alzheimer's Disease Neuroimaging Initiative dataset yields novel insights and improved predictive performance compared to traditional approaches.
  arXiv  Detail & Related papers  (2025-03-25T15:27:32Z)
• 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)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion [3.868072865207522]
  Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. We propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder. A correlation-driven loss is proposed for low-frequency feature and high-frequency feature decomposition based on embedded information.
  arXiv  Detail & Related papers  (2024-02-04T14:12:51Z)
• Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift [12.770658031721435]
  We propose a method for adapting the weights of the last layer of a pre-trained neural regression model to perform better on input data originating from a different distribution. We demonstrate how this lightweight spectral adaptation procedure can improve out-of-distribution performance for synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-12-29T04:15:58Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [89.73665256847858]
  We show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet. We also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS.
  arXiv  Detail & Related papers  (2021-07-09T19:48:23Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)

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.