Bridging the Clinical Expertise Gap: Development of a Web-Based Platform for Accessible Time Series Forecasting and Analysis

TL;DR

This paper introduces a web-based platform that simplifies time series forecasting and analysis for healthcare researchers and clinicians, making advanced techniques accessible without extensive technical expertise.

Contribution

The development of an accessible, customizable web platform that integrates multiple forecasting models and AI-driven explanations for clinical time series data analysis.

Findings

Supports multiple forecasting models and techniques

Provides AI-generated recommendations and explanations

Facilitates integration into 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.