Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study

Aug 2, 2026

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Research gaps represent unresolved questions within existing scientific knowledge, often caused by limited evidence or inconclusive findings. Conventional gap identification relies on literature reviews and expert judgments, which can be slow, labor intensive, and less suitable for rapidly evolving, time-sensitive topics. The study proposes a scalable machine learning workflow to systematically analyze scientific literature, using the COVID-19 pandemic as a case study, to cluster topics and prioritize areas for further research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

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JMIR FORMATIVE RESEARCH Abd-alrazaq et al Original Paper Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study | Alaa Abd-alrazaq1*, PhD; Abdulqadir J Nashwan2*, MSc; Zubair Shah3, PhD; Ahmad Abujaber4, PhD; Dari Alhuwail5,6, PhD; Jens Schneider3, PhD; Rawan AlSaad 1, PhD; Hazrat Ali7, PhD; Waleed Alomoush8, PhD; Arfan Ahmed 1, PhD; Sarah Aziz1, MSc | | --- | | 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar 2Department of Nursing, Hamad Medical Corporation, Doha, Qatar
3Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar 4Nursing Department, Hamad Medical Corporation, Doha, Qatar
5Information Science Department, College of Life Sciences, Kuwait University, Kuwait, Kuwait
6Health Informatics Unit, Dasman Diabetes Institute, Kuwait, Kuwait
7Faculty of Computing and Information Technology, Sohar University, Sohar, Oman 8School of Information Technology, Skyline University College, Sharjah, United Arab Emirates
*these authors contributed equally
Corresponding Author:
Alaa Abd-alrazaq, PhD
AI Center for Precision Health Weill Cornell Medicine-Qatar
A031, Weill Cornell Medicine-Qatar, Education City
Al Luqta St Doha, 23435 Qatar
Phone: 974 55708599
Email: [aaa4027@qatar-med.cornell.edu](aaa4027@qatar-med.cornell.edu)
Abstract | Background: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone tobias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. Objective: In this paper, we propose a machine learning–based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. Methods: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance) . Results: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: “virus ofCOVID-19,”“risk factors of COVID-19,” “prevention of COVID-19,” “treatment of COVID-19,” “health care delivery during COVID-19,”“and impact of COVID-19 .” The most prominent topic, observed in over half of the analyzed studies, was “the impact of COVID-19 .” [https://formative.jmir.org/2024/1/e49411](https://formative.jmir.org/2024/1/e49411) XSL• FO RenderX JMIR Form Res 2024 | vol. 8 | e49411 | p. 1 (page number not for citation purposes) JMIR FORMATIVE RESEARCH Abd-alrazaq et al Conclusions: The proposed machine learning–based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with re

FAQs

What are research gaps and why are they important in scientific research?

Research gaps are unanswered or underexplored questions caused by missing studies or inconclusive results. They provide essential starting points and motivation for further scientific inquiry.

How does the proposed approach identify research gaps using machine learning?

The approach analyzes scientific literature using the BERTopic topic modeling technique. It embeds documents, clusters them using dimensionality reduction and clustering, and represents topics by generating candidates and maximizing candidate relevance.

What data and results were obtained in the COVID-19 case study?

The study used the CORD-19 dataset containing 1,121,433 COVID-19-related papers and included 33,206 abstracts after selection. It identified 21 research gap areas grouped into 6 principal topics, with “the impact of COVID-19” being the most prominent.

Aug 2, 2026

Upload by Riley