让临床医生也能轻松做时间序列预测分析。
Bridging the Clinical Expertise Gap: Development of a Web-Based Platform for Accessible Time Series Forecasting and Analysis
- 基于网页的平台，支持数据上传与可视化分析。
- 集成多种模型与可定制训练参数，适配不同需求。
- 大语言模型提供参数推荐与结果解释，降低使用门槛。
时间序列预测在医疗等领域有广泛应用，但分析数据、建模和解读结果所需的技术专长常成为使用障碍。本文提出一个基于网页的平台，使研究人员和临床医生能轻松完成数据可视化、模型训练及结果解读。用户可上传数据并生成变量关系图；平台支持多种预测模型与高度可定制的训练方法。此外，大语言模型还能提供参数选择建议和结果解释，帮助用户理解模型输出。目标是将该平台融入学习型健康系统，实现临床数据流的持续采集与推理。
原文摘要 · Abstract (English)
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.
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