Enhancing LLMs with Smart Preprocessing for EHR Analysis

• URL: http://arxiv.org/abs/2412.02868v2
• Date: Thu, 24 Apr 2025 13:07:21 GMT
• Title: Enhancing LLMs with Smart Preprocessing for EHR Analysis
• Authors: Yixiang Qu, Yifan Dai, Shilin Yu, Pradham Tanikella, Travis Schrank, Trevor Hackman, Didong Li, Di Wu,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language processing.<n>This paper introduces a compact LLM framework optimized for local deployment in environments with stringent privacy requirements.
• Score: 3.5839042822277585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language processing; however, their application in sensitive domains such as healthcare, especially in processing Electronic Health Records (EHRs), is constrained by limited computational resources and privacy concerns. This paper introduces a compact LLM framework optimized for local deployment in environments with stringent privacy requirements and restricted access to high-performance GPUs. Our approach leverages simple yet powerful preprocessing techniques, including regular expressions (regex) and Retrieval-Augmented Generation (RAG), to extract and highlight critical information from clinical notes. By pre-filtering long, unstructured text, we enhance the performance of smaller LLMs on EHR-related tasks. Our framework is evaluated using zero-shot and few-shot learning paradigms on both private and publicly available datasets (MIMIC-IV), with additional comparisons against fine-tuned LLMs on MIMIC-IV. Experimental results demonstrate that our preprocessing strategy significantly supercharges the performance of smaller LLMs, making them well-suited for privacy-sensitive and resource-constrained applications. This study offers valuable insights into optimizing LLM performance for local, secure, and efficient healthcare applications. It provides practical guidance for real-world deployment for LLMs while tackling challenges related to privacy, computational feasibility, and clinical applicability.

Related papers

• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities [0.35998666903987897]
  This report examines the fine-tuning of Large Language Models (LLMs) It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. The report introduces a structured seven-stage pipeline for fine-tuning LLMs.
  arXiv  Detail & Related papers  (2024-08-23T14:48:02Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond [24.151927600694066]
  Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
  arXiv  Detail & Related papers  (2024-04-23T08:24:43Z)
• Developing Healthcare Language Model Embedding Spaces [0.20971479389679337]
  Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. Three methods are assessed: traditional masked language modeling, Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR) and a novel pre-training objective utilizing metadata categories from the healthcare settings. Contrastively trained models outperform other approaches on the classification tasks, delivering strong performance from limited labeled data and with fewer model parameter updates required.
  arXiv  Detail & Related papers  (2024-03-28T19:31:32Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Large Language Models as Data Preprocessors [9.99065004972981]
  Large Language Models (LLMs) have marked a significant advancement in artificial intelligence. This study explores their potential in data preprocessing, a critical stage in data mining and analytics applications. We propose an LLM-based framework for data preprocessing, which integrates cutting-edge prompt engineering techniques.
  arXiv  Detail & Related papers  (2023-08-30T23:28:43Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [11.845239346943067]
  parameter-efficient fine-tuning (PEFT) is a promising approach to efficiently specialize large language models (LLMs) to task-specific data. Our study highlights the potential for tuning larger LLMs and significant reductions in memory usage by combining PEFT with quantization.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• Improving Small Language Models on PubMedQA via Generative Data Augmentation [4.96649519549027]
  Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing. Small Language Models (SLMs) are known for their efficiency, but they often struggle with limited capacity and training data. We introduce a novel method aimed at improving SLMs in the medical domain using LLM-based generative data augmentation.
  arXiv  Detail & Related papers  (2023-05-12T23:49:23Z)

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.