Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models

• URL: http://arxiv.org/abs/2505.09805v1
• Date: Wed, 14 May 2025 21:05:40 GMT
• Title: Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models
• Authors: Aditya Nagori, Ayush Gautam, Matthew O. Wiens, Vuong Nguyen, Nathan Kenya Mugisha, Jerome Kabakyenga, Niranjan Kissoon, John Mark Ansermino, Rishikesan Kamaleswaran,
• Abstract summary: This study evaluates Large Language Model (LLM) based clustering against classical methods.<n>Patient records were serialized into text with and without a clustering objective.<n>LLAMA 3.1 8B with the clustering objective performed better with higher number of clusters.
• Score: 2.593361890114316
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimensional, heterogeneous healthcare data and lack contextual understanding. This study evaluates Large Language Model (LLM) based clustering against classical methods using a pediatric sepsis dataset from a low-income country (LIC), containing 2,686 records with 28 numerical and 119 categorical variables. Patient records were serialized into text with and without a clustering objective. Embeddings were generated using quantized LLAMA 3.1 8B, DeepSeek-R1-Distill-Llama-8B with low-rank adaptation(LoRA), and Stella-En-400M-V5 models. K-means clustering was applied to these embeddings. Classical comparisons included K-Medoids clustering on UMAP and FAMD-reduced mixed data. Silhouette scores and statistical tests evaluated cluster quality and distinctiveness. Stella-En-400M-V5 achieved the highest Silhouette Score (0.86). LLAMA 3.1 8B with the clustering objective performed better with higher number of clusters, identifying subgroups with distinct nutritional, clinical, and socioeconomic profiles. LLM-based methods outperformed classical techniques by capturing richer context and prioritizing key features. These results highlight potential of LLMs for contextual phenotyping and informed decision-making in resource-limited settings.

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