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Related Concept Videos

Clinical Trials01:16

Clinical Trials

11.2K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Related Experiment Video

Updated: Apr 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

Madhusudan Ghosh1, Shrimon Mukherjee1, Asmit Ganguly2

  • 1School of Mathematical and Computational Sciences, Indian Association for the Cultivation of Science, India.

Methods (San Diego, Calif.)
|April 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces unsupervised and instruction-tuned Large Language Models (LLMs) for automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trials. The LLM-based methods streamline systematic reviews by bypassing manual data annotation, achieving state-of-the-art results.

Keywords:
Bio-medical NERIn-context learningInstruction tuningLLMLlamaPICO frame extraction

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Area of Science:

  • Natural Language Processing (NLP)
  • Biomedical Informatics
  • Clinical Trial Analysis

Background:

  • Systematic reviews are crucial for evidence-based medicine but are hindered by the increasing volume of clinical trial publications.
  • Manual extraction of Population, Intervention, Comparator, and Outcome (PICO) elements is time-consuming and labor-intensive.
  • Existing automated PICO extraction methods often require extensive manually annotated datasets, limiting their scalability.

Purpose of the Study:

  • To develop and evaluate unsupervised and supervised Large Language Model (LLM) frameworks for automated PICO extraction from clinical trial reports.
  • To leverage the pre-trained knowledge of LLMs for PICO term extraction in an unsupervised setting, reducing reliance on labeled data.
  • To demonstrate the effectiveness of instruction tuning with Low Rank Adaptation (LORA) for high-performance PICO extraction in resource-constrained environments.

Main Methods:

  • Utilized In-Context Learning (ICL) with a pre-trained LLM (AlpaCare) for unsupervised PICO term extraction.
  • Employed instruction tuning with LORA on AlpaCare for supervised PICO term extraction, optimizing performance in low-resource settings.
  • Evaluated both ICL and instruction-tuned frameworks on diverse PICO datasets (EBM-NLP, EBM-COMET, EBM-NLPrev, EBM-NLPh).

Main Results:

  • The unsupervised ICL-based framework achieved comparable results across various EBM-NLP dataset versions.
  • The instruction-tuned framework, utilizing LORA, demonstrated state-of-the-art performance on all tested EBM-NLP datasets.
  • Both methods successfully extracted PICO-related terminologies from clinical trial documents, showcasing LLM efficacy.

Conclusions:

  • LLM-based approaches, particularly instruction tuning with LORA, offer a powerful and efficient solution for automated PICO extraction.
  • These methods significantly reduce the manual effort required for systematic reviews, accelerating evidence synthesis.
  • The developed frameworks provide a scalable and effective tool for researchers analyzing clinical trial data.