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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

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A new iterative method to reduce workload in systematic review process.

Siddhartha Jonnalagadda¹, Diana Petitti

¹Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55902, USA. siddharta@mayo.edu

International Journal of Computational Biology and Drug Design

|February 23, 2013

Summary

This summary is machine-generated.

Automating biomedical literature reviews with natural language processing significantly reduces workload. Our novel system uses reviewer feedback to refine searches, decreasing the number of articles needing manual review.

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

Biomedical Informatics
Natural Language Processing
Systematic Review Methodology

Background:

Systematic reviews of biomedical literature are costly and time-consuming.
Current automated classification methods require specific training for each review, increasing workload.
There is a need for efficient, adaptable tools to streamline literature review processes.

Purpose of the Study:

To develop and evaluate a natural language processing system that reduces the workload of systematic literature reviews.
To create a system that learns and adapts using only human reviewer input and feedback.
To improve the efficiency of identifying relevant publications for systematic reviews.

Main Methods:

A novel system employing natural language processing and relevance feedback algorithms.
The system modifies search queries based on human reviewer classifications during the review process.
Presents semantically closest documents to the query for reviewer assessment.

Main Results:

Substantial reduction in the number of articles requiring manual review across 15 drug systematic reviews.
Achieved significant workload reduction, with reviewed articles ranging from 6% to 30% for 95% recall.
Demonstrated the effectiveness of the adaptive, feedback-driven approach.

Conclusions:

The proposed system effectively reduces the manual workload in biomedical systematic reviews.
Relevance feedback and natural language processing offer a more efficient alternative to traditional methods.
This approach enhances the feasibility and speed of conducting systematic literature reviews.