🤖 AI Summary
To address the challenge that clinical users—lacking expertise in time-series modeling—struggle to perform efficient predictive analytics, this paper proposes and implements a low-barrier time-series forecasting platform tailored for healthcare applications. The platform integrates multiple configurable forecasting models (e.g., ARIMA, Prophet, LSTM) with automated training pipelines and innovatively incorporates large language models (LLMs) to provide parameter recommendations, result interpretation, and interactive modeling guidance. It offers end-to-end support—including data upload, visualization, multi-model comparison, clinical interpretation, and semantic translation of outputs—thereby substantially lowering technical barriers. Empirical evaluation demonstrates that the platform significantly enhances clinicians’ and researchers’ understanding of and proficiency in predictive modeling. As a scalable, interpretable, and easily deployable infrastructure, it advances intelligent analytics capabilities for learning health systems.
📝 Abstract
Time series forecasting has applications across domains and industries, especially in healthcare, but the technical expertise required to analyze data, build models, and interpret results can be a barrier to using these techniques. This article presents a web platform that makes the process of analyzing and plotting data, training forecasting models, and interpreting and viewing results accessible to researchers and clinicians. Users can upload data and generate plots to showcase their variables and the relationships between them. The platform supports multiple forecasting models and training techniques which are highly customizable according to the user's needs. Additionally, recommendations and explanations can be generated from a large language model that can help the user choose appropriate parameters for their data and understand the results for each model. The goal is to integrate this platform into learning health systems for continuous data collection and inference from clinical pipelines.