AI Article Synopsis
- Patient Health Records (PHRs) allow patients to manage their health information, but making sense of this extensive data can be challenging for both patients and doctors.
- A new study introduces a generative pre-trained transformer-based model to efficiently summarize health data from PHRs.
- The model, after fine-tuning, shows promising results using a smaller dataset, offering a novel approach to improve information management in healthcare.
Article Abstract
Although Patient Health Records (PHRs) are vital tools for patients, enabling them to access and manage health information, it remains challenging for doctors and patients to gather a swift overview of a patient's health status based on the extensive information included in the PHR. Our study introduces a generative pre-trained transformer-based language model to summarize health information documented in previously developed PHRs efficiently. By fine-tuning the model, we achieved results comparable to those of other studies in this domain, despite utilizing a smaller dataset. This data-to-text application represents a novel method that can be expected to promote enhanced information management in the medical field.
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| http://dx.doi.org/10.3233/SHTI240504 | DOI Listing |
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