Medical Journal of the Islamic Republic Of Iran

en A Scoping Review of Adopted Information Extraction Methods for RCTs Computer scientist and technologist Computer scientist and technologist Review Article Review Article Background: Randomized controlled trials (RCTs) provide the strongest evidence for therapeutic interventions and their effects on groups of subjects. However, the large amount of unstructured information in these trials makes it challenging and time-consuming to make decisions and identify important concepts and valid evidence. This study aims to explore methods for automating or semi-automating information extraction from reports of RCT studies.    Methods: We conducted a systematic search of PubMed, ACM Digital Library, and Web of Science to identify relevant articles published between January 1, 2010, and 2022. We focused on published Natural Language Processing (NLP), machine learning, and deep learning methods that automate or semi-automate key elements of information extraction in the context of RCTs.    Results: A total of 26 publications were included, which discussed the automatic extraction of key characteristics of RCTs using various PICO frameworks (PIBOSO and PECODR). Among these publications, 14 (53.8%) extracted key characteristics based on PICO, PIBOSO, and PECODR, while 12 (46.1%) discussed information extraction methods in RCT studies. Common approaches mentioned included word/phrase matching, machine learning algorithms such as binary classification using the Naïve Bayes algorithm and powerful BERT network for feature extraction, support vector machine for data classification, conditional random field, non-machine-dependent automation, and machine learning or deep learning approaches.    Conclusion: The lack of publicly available software and limited access to existing software makes it difficult to determine the most powerful information extraction system. However, deep learning models like Transformers and BERT language models have shown better performance in natural language processing. <div></div> Information extraction, NLP, Randomized Controlled Trials, automation 743 754 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-8044-1&slc_lang=en&sid=1 Azadeh Aletaha azadeh.aletaha@gmail.com 200319475328460079398 200319475328460079398 No Department of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, & Evidence-Based Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran Leila Nemati-Anaraki Nematianaraki.l@iums.ac.ir 200319475328460079399 200319475328460079399 Yes Department of Medical Library and Information Science, School of Health Management and Information Sciences, & Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran AbbasAli Keshtkar abkeshtkar@gmail.com 200319475328460079400 200319475328460079400 No Department of Health Science Educational Development, School of Public Health, Tehran University of Medical Sciences. Tehran, Iran Shahram Sedghi shahramsedghi@gmail.com 200319475328460079401 200319475328460079401 No Department of Medical Library and Information Science, School of Health Management and Information Sciences, & Economics Research Center, Iran University of Medical Sciences, PO Box 14665-354, Tehran, Iran Abdalsamad Keramatfar keramatfar.a.s@gmail.com 200319475328460079402 200319475328460079402 No Department of Data Analytics, Scientific Information Database (SID), Tehran, Iran Anna Korolyova aakorolyova@gmail.com 200319475328460079403 200319475328460079403 No Computer Science Laboratory for Mechanics and Engineering Sciences (LIMSI), CNRS, Universit´e Paris-Saclay, F-91405 Orsay, France & School of Life Sciences and Facility Management Zurich University of Applied Sciences (ZHAW) & Fraser House, White Cross Business Park, Lancaster, LA1 4XQ