A case study on using a large language model to analyze continuous glucose monitoring data
Abstract Continuous glucose monitors (CGM) provide valuable insights about glycemic control that aid in diabetes management. However, interpreting metrics and charts and synthesizing them into linguistic summaries is often non-trivial for patients and providers. The advent of large language models (...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84003-0 |
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| author | Elizabeth Healey Amelia Li Min Tan Kristen L. Flint Jessica L. Ruiz Isaac Kohane |
| author_facet | Elizabeth Healey Amelia Li Min Tan Kristen L. Flint Jessica L. Ruiz Isaac Kohane |
| author_sort | Elizabeth Healey |
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| description | Abstract Continuous glucose monitors (CGM) provide valuable insights about glycemic control that aid in diabetes management. However, interpreting metrics and charts and synthesizing them into linguistic summaries is often non-trivial for patients and providers. The advent of large language models (LLMs) has enabled real-time text generation and summarization of medical data. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. GPT-4 performed 9 out of the 10 quantitative metrics tasks with perfect accuracy across all 10 cases. The clinician-evaluated CGM analysis tasks had good performance across measures of accuracy [lowest task mean score 8/10, highest task mean score 10/10], completeness [lowest task mean score 7.5/10, highest task mean score 10/10], and safety [lowest task mean score 9.5/10, highest task mean score 10/10]. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through data summarization and, more broadly, the potential to leverage LLMs for streamlined medical time series analysis. |
| format | Article |
| id | doaj-art-c8ffd82dc27d4febbd4e9bcdc49845cd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c8ffd82dc27d4febbd4e9bcdc49845cd2025-01-12T12:21:32ZengNature PortfolioScientific Reports2045-23222025-01-011511710.1038/s41598-024-84003-0A case study on using a large language model to analyze continuous glucose monitoring dataElizabeth Healey0Amelia Li Min Tan1Kristen L. Flint2Jessica L. Ruiz3Isaac Kohane4Program in Health Sciences and Technology, Massachusetts Institute of Technology Department of Biomedical Informatics, Harvard Medical School Diabetes Research Center, Massachusetts General Hospital Division of Endocrinology, Boston Children’s Hospital Department of Biomedical Informatics, Harvard Medical SchoolAbstract Continuous glucose monitors (CGM) provide valuable insights about glycemic control that aid in diabetes management. However, interpreting metrics and charts and synthesizing them into linguistic summaries is often non-trivial for patients and providers. The advent of large language models (LLMs) has enabled real-time text generation and summarization of medical data. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. GPT-4 performed 9 out of the 10 quantitative metrics tasks with perfect accuracy across all 10 cases. The clinician-evaluated CGM analysis tasks had good performance across measures of accuracy [lowest task mean score 8/10, highest task mean score 10/10], completeness [lowest task mean score 7.5/10, highest task mean score 10/10], and safety [lowest task mean score 9.5/10, highest task mean score 10/10]. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through data summarization and, more broadly, the potential to leverage LLMs for streamlined medical time series analysis.https://doi.org/10.1038/s41598-024-84003-0 |
| spellingShingle | Elizabeth Healey Amelia Li Min Tan Kristen L. Flint Jessica L. Ruiz Isaac Kohane A case study on using a large language model to analyze continuous glucose monitoring data Scientific Reports |
| title | A case study on using a large language model to analyze continuous glucose monitoring data |
| title_full | A case study on using a large language model to analyze continuous glucose monitoring data |
| title_fullStr | A case study on using a large language model to analyze continuous glucose monitoring data |
| title_full_unstemmed | A case study on using a large language model to analyze continuous glucose monitoring data |
| title_short | A case study on using a large language model to analyze continuous glucose monitoring data |
| title_sort | case study on using a large language model to analyze continuous glucose monitoring data |
| url | https://doi.org/10.1038/s41598-024-84003-0 |
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