Leveraging Large Language Models for Departmental Classification of Medical Records
This research develops large language models (LLMs) to alleviate the workload of healthcare professionals by classifying medical records into their departments. The models utilize medical records as a dataset for fine-tuning and use clinical knowledge bases to enhance accuracy and efficiency in iden...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6525 |
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| Summary: | This research develops large language models (LLMs) to alleviate the workload of healthcare professionals by classifying medical records into their departments. The models utilize medical records as a dataset for fine-tuning and use clinical knowledge bases to enhance accuracy and efficiency in identifying appropriate departments. This study explores the integration of advanced large language models (LLMs) with quantized low-rank adaptation (QLoRA) for efficient training. The medical department classifier demonstrated impressive performance in diagnosing medical conditions, with an accuracy of 96.26. The findings suggest that LLM-based solutions could significantly improve the efficiency of clinical consultations. What is more, the trained models are hosted on GitHub and are publicly available for use, empowering the wider community to benefit from this research. |
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| ISSN: | 2076-3417 |