Journal
INFORMATION
Volume 13, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/info13030117
Keywords
natural language processing; health data; systematic reviews; guidelines; evidence-based medicine; COVID-19; SARS-CoV-2; machine reading; machine learning; artificial intelligence
Categories
Funding
- Ministero dell'Universita e della Ricerca (Italian Ministry of University and Research, MUR) within the Fondo Integrativo Speciale per la Ricerca (FIRS) [CUP J55F21001990001-COKE_FISR2020IP_00621]
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This article emphasizes the importance of accelerating and streamlining the extraction and synthesis of scientific evidence, particularly in the systematic review process. It introduces a semi-automated system using machine reading and deep learning to support literature review and guideline drafting processes.
The COVID-19 pandemic highlighted the importance of validated and updated scientific information to help policy makers, healthcare professionals, and the public. The speed in disseminating reliable information and the subsequent guidelines and policy implementation are also essential to save as many lives as possible. Trustworthy guidelines should be based on a systematic evidence review which uses reproducible analytical methods to collect secondary data and analyse them. However, the guidelines' drafting process is time consuming and requires a great deal of resources. This paper aims to highlight the importance of accelerating and streamlining the extraction and synthesis of scientific evidence, specifically within the systematic review process. To do so, this paper describes the COKE (COVID-19 Knowledge Extraction framework for next generation discovery science) Project, which involves the use of machine reading and deep learning to design and implement a semi-automated system that supports and enhances the systematic literature review and guideline drafting processes. Specifically, we propose a framework for aiding in the literature selection and navigation process that employs natural language processing and clustering techniques for selecting and organizing the literature for human consultation, according to PICO (Population/Problem, Intervention, Comparison, and Outcome) elements. We show some preliminary results of the automatic classification of sentences on a dataset of abstracts related to COVID-19.
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