A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoods

• URL: http://arxiv.org/abs/2602.00060v1
• Date: Tue, 20 Jan 2026 06:11:35 GMT
• Title: A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoods
• Abstract summary: GEOFRAIL is a longitudinal dataset collected from community-dwelling frail older adults following hospital discharge.<n>Data was collected over an eight-week post-discharge period using standardized pipelines with privacy-preserving spatial aggregation.
• Score: 3.753262097971202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Frailty in older adults is associated with increased vulnerability to functional decline, reduced mobility, social isolation, and challenges during the transition from hospital to community living. These factors are associated with rehospitalization and may adversely influence recovery. Neighborhood environments can further shape recovery trajectories by affecting mobility opportunities, social engagement, and access to community resources. Multimodal sensing technologies combined with data-driven analytical approaches offer the potential to continuously monitor these multidimensional factors in real-world settings. This Data Descriptor presents GEOFRAIL, a longitudinal geospatial multimodal dataset collected from community-dwelling frail older adults following hospital discharge. The dataset is organized into interconnected tables capturing participant demographics, features derived from multimodal sensors, biweekly clinical assessments of frailty, physical function, and social isolation, and temporal location records linked to neighborhood amenities, crime rates, and census-based socioeconomic indicators. Data were collected over an eight-week post-discharge period using standardized pipelines with privacy-preserving spatial aggregation. Technical validation demonstrates internal consistency across geospatial, sensor-derived, and clinical measures and reports baseline performance of machine learning models for characterizing recovery trajectories.

Related papers

• MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification [60.41879345743941]
  We propose MedMamba, an end-to-end architecture that integrates state space models with domain-specific inductive biases.<n>MedMamba achieves state-of-the-art performance while maintaining linear computational complexity.
  arXiv  Detail & Related papers  (2026-05-24T09:23:39Z)
• Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification [6.345037597566314]
  We present the Bi-Axial Transformer (BAT) which attends to both the clinical variable and time point axes of EHR data to learn richer data relationships.<n>BAT achieves state-of-the-art performance on sepsis prediction and is competitive to top methods for mortality classification.
  arXiv  Detail & Related papers  (2025-08-17T16:04:56Z)
• Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs [1.1060425537315088]
  We propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations.<n>Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales.<n>Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields.
  arXiv  Detail & Related papers  (2025-03-24T11:29:06Z)
• Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings [2.166000001057538]
  Lower-Limb Fractures (LLF) are a major health concern for older adults.<n>During recovery, older adults frequently face social isolation and functional decline.<n>This paper presents a new publicly available multi-modal sensor dataset, MAISON-LLF.
  arXiv  Detail & Related papers  (2025-01-23T18:01:01Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation [103.55894890759376]
  This paper introduces several building blocks that use representation learning to handle the heterogeneous feature spaces. We show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners.
  arXiv  Detail & Related papers  (2022-10-08T16:41:02Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)

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.