Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation

• URL: http://arxiv.org/abs/2409.09324v2
• Date: Sat, 28 Sep 2024 02:16:19 GMT
• Title: Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation
• Authors: Hui Yi Leong, Yi Fan Gao, Ji Shuai, Yang Zhang, Uktu Pamuksuz,
• Abstract summary: This study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, MediGen achieves high accuracy in transcribing and summarizing clinical interactions.
• Score: 6.180195560275004
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.

Related papers

• Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge [6.977177904883792]
  AMG-RAG is a comprehensive framework that automates the construction and continuous updating of medical knowledge graphs. It integrates reasoning, and retrieves current external evidence, such as PubMed and WikiSearch. It achieves an F1 score of 74.1 percent on MEDQA and an accuracy of 66.34 percent on MEDMCQA, outperforming both comparable models and those 10 to 100 times larger.
  arXiv  Detail & Related papers  (2025-02-18T16:29:45Z)
• A GEN AI Framework for Medical Note Generation [3.7444770630637167]
  MediNotes is an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio.
  arXiv  Detail & Related papers  (2024-09-27T23:05:02Z)
• Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering [13.237829215746443]
  Sentence-t5 + Mistral 7B model excels in understanding and generating precise medical answers. Our findings highlight the potential of integrating sophisticated LLMs in medical contexts.
  arXiv  Detail & Related papers  (2024-08-08T00:35:39Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients [1.379398224469229]
  This study addresses inefficiencies and inaccuracies in creating discharge notes manually, particularly for cardiac patients. Our research evaluates the capability of large language model (LLM) to enhance the documentation process. Among the various models assessed, Mistral-7B distinguished itself by accurately generating discharge notes.
  arXiv  Detail & Related papers  (2024-04-08T01:55:28Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models [91.25119823784705]
  Large language models (LLMs) can potentially democratize access to medical knowledge. We release MEDITRON: a suite of open-source LLMs with 7B and 70B parameters adapted to the medical domain.
  arXiv  Detail & Related papers  (2023-11-27T18:49:43Z)
• Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting [0.49478969093606673]
  Two-shot prompting approach in combination with scope and domain context outperforms other methods. The automated reports are approximately twice as long as the human references.
  arXiv  Detail & Related papers  (2023-11-22T09:51:53Z)
• ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences [51.66185471742271]
  We propose ChiMed-GPT, a benchmark LLM designed explicitly for Chinese medical domain. ChiMed-GPT undergoes a comprehensive training regime with pre-training, SFT, and RLHF. We analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients.
  arXiv  Detail & Related papers  (2023-11-10T12:25:32Z)
• MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records [60.35217378132709]
  Large language models (LLMs) can follow natural language instructions with human-level fluency. evaluating LLMs on realistic text generation tasks for healthcare remains challenging. We introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data.
  arXiv  Detail & Related papers  (2023-08-27T12:24:39Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.