Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918113301233664 |
|---|---|
| author | Nagori, Aditya Gautam, Ayush Wiens, Matthew O. Nguyen, Vuong Mugisha, Nathan Kenya Kabakyenga, Jerome Kissoon, Niranjan Ansermino, John Mark Kamaleswaran, Rishikesan |
| author_facet | Nagori, Aditya Gautam, Ayush Wiens, Matthew O. Nguyen, Vuong Mugisha, Nathan Kenya Kabakyenga, Jerome Kissoon, Niranjan Ansermino, John Mark Kamaleswaran, Rishikesan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models Nagori, Aditya Gautam, Ayush Wiens, Matthew O. Nguyen, Vuong Mugisha, Nathan Kenya Kabakyenga, Jerome Kissoon, Niranjan Ansermino, John Mark Kamaleswaran, Rishikesan Quantitative Methods Artificial Intelligence Computation and Language Machine Learning Applications 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. |
| title | Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models |
| topic | Quantitative Methods Artificial Intelligence Computation and Language Machine Learning Applications |
| url | https://arxiv.org/abs/2505.09805 |