Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription

• URL: http://arxiv.org/abs/2503.03687v2
• Date: Sun, 03 Aug 2025 08:19:53 GMT
• Title: Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription
• Authors: Zihao Zhao, Chenxiao Fan, Junlong Liu, Zheng Wang, Xiangnan He, Chongming Gao, Juan Li, Fuli Feng,
• Abstract summary: Large language models (LLMs) hold significant promise in achieving general medication recommendation systems.<n>We introduce Language-Assisted Medication Recommendation, which tailors LLMs for medication recommendation in a medication-aware manner.<n>Fine-tuning LLMs with this framework can outperform existing methods by more than 10% in internal validation and generalize across temporal and external validations.
• Score: 45.41664696802343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flexibility to medication encoding. We evaluated both general-purpose and medical-specific LLMs for medication recommendations, showing their unsatisfactory precision and severe overprescription. To address this, we introduce Language-Assisted Medication Recommendation, which tailors LLMs for medication recommendation in a medication-aware manner, improving the usage of clinical notes. Fine-tuning LLMs with this framework can outperform existing methods by more than 10% in internal validation and generalize across temporal and external validations. Furthermore, the model maintains high accuracy when encountering out-of-distribution medication.

Related papers

• Fine-grained List-wise Alignment for Generative Medication Recommendation [30.397147691681607]
  We propose FLAME, a fine-grained list-wise alignment framework for large language models (LLMs)<n> FLAME formulates recommendation as a sequential decision process, where each step adds or removes a single drug.<n> Experiments on benchmark datasets demonstrate that FLAME achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-05-26T16:59:23Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Natural Language-Assisted Multi-modal Medication Recommendation [97.07805345563348]
  We introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR)<n>The NLA-MMR is a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly.<n>In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications.
  arXiv  Detail & Related papers  (2025-01-13T09:51:50Z)
• MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models [0.0]
  Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications.<n>Their propensity for hallucinations presents substantial risks to patient care.<n>This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs.
  arXiv  Detail & Related papers  (2024-12-25T16:51:29Z)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications [2.838746648891565]
  We introduce MEDIC, a framework assessing Large Language Models (LLMs) across five critical dimensions of clinical competence. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths.
  arXiv  Detail & Related papers  (2024-09-11T14:44:51Z)
• Enhancing Medication Recommendation with LLM Text Representation [0.0]
  We propose a method of enhancing medication recommendation with Large Language Model (LLM) text representation. LLM harnesses powerful language understanding and generation capabilities, enabling the extraction of information from complex and lengthy unstructured data.
  arXiv  Detail & Related papers  (2024-07-15T05:51:11Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models [29.05425041393475]
  Generative Large Language Models (LLMs) hold significant promise in healthcare. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center.
  arXiv  Detail & Related papers  (2024-01-05T15:09:57Z)
• LLMs Accelerate Annotation for Medical Information Extraction [7.743388571513413]
  We propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating ground truth labels for medical text annotation. We rigorously evaluate our method on a medical information extraction task, demonstrating that our approach not only substantially cuts down on human intervention but also maintains high accuracy.
  arXiv  Detail & Related papers  (2023-12-04T19:26:13Z)
• 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)
• Aligning Large Language Models for Clinical Tasks [0.0]
  Large Language Models (LLMs) have demonstrated remarkable adaptability, showcasing their capacity to excel in tasks for which they were not explicitly trained. We propose an alignment strategy for medical question-answering, known as 'expand-guess-refine' A preliminary analysis of this method demonstrated outstanding performance, achieving a score of 70.63% on a subset of questions sourced from the USMLE dataset.
  arXiv  Detail & Related papers  (2023-09-06T10:20:06Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)

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.