AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs

TL;DR

This paper introduces AlpaPICO, a novel method using large language models for extracting PICO frames from clinical trial documents, achieving high accuracy in both unsupervised and low-resource supervised settings.

Contribution

It leverages in-context learning with LLMs for unsupervised PICO extraction and employs instruction tuning with LoRA for state-of-the-art results in low-resource environments.

Findings

ICL-based framework achieves comparable results to supervised methods.

Instruction tuning with LoRA yields state-of-the-art performance.

The approach reduces reliance on manually annotated data.

Abstract

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at \url{https://github.com/shrimonmuke0202/AlpaPICO.git}.