Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
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Abstract (arXiv)
In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially given the fine temporal granularity of the data. In this study, we focus on a dataset of home activity records from people living with Dementia. We propose a two-stage self-supervised learning approach. The first stage involves converting time-series activities into text strings, which are then encoded by a fine-tuned language model. In the second stage, these time-series vectors are bi-dimensionalized for applying PageRank method, to analyze latent state transitions to quantitatively assess participants behavioral patterns and identify activity biases. These insights, combined with diagnostic data, aim to support personalized care interventions.
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References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 269
- 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 100
- 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Modelscited by 65
- 2020Machine Learning Advances for Time Series Forecastingcited by 49
- 2024Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learningcited by 25
- 2018Improving Clinical Predictions through Unsupervised Time Series Representation Learningcited by 24
- 2024Representation Learning of Daily Movement Data Using Text Encoderscited by 2
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