Utilizing Large Language Models (LLMs) in Data Analysis Pipeline for Digital Phenotyping: Description, Prediction, and Visualization

Digital phenotyping is the "moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices," according to Onnela and Rauch. Digital phenotyping research has historically contained many entry barriers due to high costs and complexity. However, the growing popularity of personal devices such as mobile phones has enabled researchers to collect participant data with more convenience and lower costs than ever before. This paper presents the Intelligent Phenotype Analysis Suite (IPAS), a new AI-powered tool for streamlined phenotype investigation. This tool provides researchers the ability to generate descriptions, predictions, and visualizations of their digital phenotyping data through a simple, novel chat-bot interface. IPAS combines an array of data science techniques with natural language processing capabilities of large language models to accelerate the data analysis process for researchers. IPAS extracts raw Beiwe data, an intuitive data collection platform which only requires participants to install an application on their mobile phone and permit data collection. Furthermore, we evaluate the accuracy of IPAS by using key LLM performance metrics: Precision, Recall, and F1 Score. While testing, IPAS sometimes struggled to generate code. However, in all test prompts, IPAS correctly identified the pre-written function needed to perform the requested task. Altogether, IPAS improves prior methods by enabling researchers of all levels of experience to analyze digital phenotyping data using natural language queries.