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

Article

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

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Citation

Abd-alrazaq A, Nashwan AJ, Shah Z, Abujaber A, Alhuwail D, Schneider J, AlSaad R, Ali H, Alomoush W, Ahmed A & Aziz S (2024) Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study. JMIR Formative Research, 8, Art. No.: e49411. https://doi.org/10.2196/49411

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 to bias. 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 of COVID-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.” 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 research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Keywords
research gaps; research gap; research topic; research topics; scientific literature; literature review; machine learning; COVID-19; BERTopic; topic clustering; text analysis; BERT; NLP; natural language processing; review methods; review methodology; SARS-CoV-2; coronavirus; COVID

Journal
JMIR Formative Research: Volume 8