# Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models

arXiv: https://arxiv.org/abs/2506.12156 · HTML: https://mlsanity.com/paper/2506.12156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data](https://mlsanity.com/paper/2403.01133/) (2024)
- [Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings](https://mlsanity.com/paper/2501.13888/) (2025)

## Built on by (most cited first, 1 of 1)

- [Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement](https://mlsanity.com/paper/2608.23531/) (2026)

## Abstract (arXiv)

Interpreting large volumes of high-dimensional, unlabeled data in a manner that is comprehensible to humans remains a significant challenge across various domains. In unsupervised healthcare data analysis, interpreting clustered data can offer meaningful insights into patients' health outcomes, which hold direct implications for healthcare providers. This paper addresses the problem of interpreting clustered sensor data collected from older adult patients recovering from lower-limb fractures in the community. A total of 560 days of multimodal sensor data, including acceleration, step count, ambient motion, GPS location, heart rate, and sleep, alongside clinical scores, were remotely collected from patients at home. Clustering was first carried out separately for each data modality to assess the impact of feature sets extracted from each modality on patients' recovery trajectories. Then, using context-aware prompting, a large language model was employed to infer meaningful cluster labels for the clusters derived from each modality. The quality of these clusters and their corresponding labels was validated through rigorous statistical testing and visualization against clinical scores collected alongside the multimodal sensor data. The results demonstrated the statistical significance of most modality-specific cluster labels generated by the large language model with respect to clinical scores, confirming the efficacy of the proposed method for interpreting sensor data in an unsupervised manner. This unsupervised data analysis approach, relying solely on sensor data, enables clinicians to identify at-risk patients and take timely measures to improve health outcomes.