Prompting Large Language Models for Zero-Shot Clinical Prediction with
Structured Longitudinal Electronic Health Record Data
- URL: http://arxiv.org/abs/2402.01713v2
- Date: Sat, 10 Feb 2024 16:31:40 GMT
- Title: Prompting Large Language Models for Zero-Shot Clinical Prediction with
Structured Longitudinal Electronic Health Record Data
- Authors: Yinghao Zhu, Zixiang Wang, Junyi Gao, Yuning Tong, Jingkun An, Weibin
Liao, Ewen M. Harrison, Liantao Ma, Chengwei Pan
- Abstract summary: Large Language Models (LLMs) are traditionally tailored for natural language processing.
This research investigates the adaptability of LLMs, like GPT-4, to EHR data.
In response to the longitudinal, sparse, and knowledge-infused nature of EHR data, our prompting approach involves taking into account specific characteristics.
- Score: 7.815738943706123
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The inherent complexity of structured longitudinal Electronic Health Records
(EHR) data poses a significant challenge when integrated with Large Language
Models (LLMs), which are traditionally tailored for natural language
processing. Motivated by the urgent need for swift decision-making during new
disease outbreaks, where traditional predictive models often fail due to a lack
of historical data, this research investigates the adaptability of LLMs, like
GPT-4, to EHR data. We particularly focus on their zero-shot capabilities,
which enable them to make predictions in scenarios in which they haven't been
explicitly trained. In response to the longitudinal, sparse, and
knowledge-infused nature of EHR data, our prompting approach involves taking
into account specific EHR characteristics such as units and reference ranges,
and employing an in-context learning strategy that aligns with clinical
contexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets
demonstrate that with our elaborately designed prompting framework, LLMs can
improve prediction performance in key tasks such as mortality, length-of-stay,
and 30-day readmission by about 35\%, surpassing ML models in few-shot
settings. Our research underscores the potential of LLMs in enhancing clinical
decision-making, especially in urgent healthcare situations like the outbreak
of emerging diseases with no labeled data. The code is publicly available at
https://github.com/yhzhu99/llm4healthcare for reproducibility.
Related papers
- SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research [45.2233252981348]
Large Language Models have shown promising results in their ability to encode general medical knowledge.
We test the ability of state-of-the-art LLMs to leverage their internal knowledge and reasoning for epilepsy diagnosis.
arXiv Detail & Related papers (2024-07-03T11:02:12Z) - EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling [22.94521527609479]
EMERGE is a Retrieval-Augmented Generation driven framework aimed at enhancing multimodal EHR predictive modeling.
Our approach extracts entities from both time-series data and clinical notes by prompting Large Language Models.
The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses.
arXiv Detail & Related papers (2024-05-27T10:53:15Z) - REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records
Analysis via Large Language Models [19.62552013839689]
Existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge.
We propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations.
Our experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines.
arXiv Detail & Related papers (2024-02-10T18:27:28Z) - Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)
Our research aims to transform existing medication recommendation methodologies using LLMs.
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) - Clinical Risk Prediction Using Language Models: Benefits And
Considerations [23.781690889237794]
This study focuses on using structured descriptions within vocabularies to make predictions exclusively based on that information.
We find that employing LMs to represent structured EHRs leads to improved or at least comparable performance in diverse risk prediction tasks.
arXiv Detail & Related papers (2023-11-29T04:32:19Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - Interpretable Medical Diagnostics with Structured Data Extraction by
Large Language Models [59.89454513692417]
Tabular data is often hidden in text, particularly in medical diagnostic reports.
We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM.
We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
arXiv Detail & Related papers (2023-06-08T09:12:28Z) - Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning.
They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health.
Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
arXiv Detail & Related papers (2023-05-30T22:05:11Z) - 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) - SANSformers: Self-Supervised Forecasting in Electronic Health Records
with Attention-Free Models [48.07469930813923]
This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities.
We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data.
Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
arXiv Detail & Related papers (2021-08-31T08:23:56Z) - Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal
Health Event Prediction [13.24834156675212]
We propose a hyperbolic embedding method with information flow to pre-train medical code representations in a hierarchical structure.
We incorporate these pre-trained representations into a graph neural network to detect disease complications.
We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data.
arXiv Detail & Related papers (2021-06-09T00:42:44Z)
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.