Literature-based discovery approaches for evidence-based healthcare: a systematic review

Abstract

Purpose Literature-Based Discovery (LBD) is a text mining technique used to generate novel hypotheses from vast amounts of literature sources, by identifying links between concepts from disparate sources. One of the main areas where it has been predominantly applied is the healthcare domain, whereby promising results, in the form of novel hypotheses, have been reported. The purpose of this work was to conduct a systematic literature review of recent publications on LBD in the healthcare domain in order to assess the trends in the approaches used and to identify issues and challenges for such systems. Methods The review was conducted following the principles of the Kitchenham method. The selected studies have been scrutinized and the derived findings have been reported following the PRISMA guidelines. Results The review results reveal useful information regarding the application areas, the data sources considered, the approaches used, the performance in terms of accuracy and reliability and future research challenges. The results of this review will be beneficial to LBD researchers and other stakeholders in the healthcare domain, by providing them with useful insights on the approaches to adopt, data sources to consider, evaluation model to use and challenges to reflect on. Conclusion The synthesis of the results of this work has shed light on recent issues and challenges that drive new LBD models and provides avenues for their application in other diverse areas in the healthcare domain. To the best of our knowledge, no such recent review has been conducted.

Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

“…LBD has been conducted for almost forty years (Smalheiser et al, 2023;Henry & McInnes, 2017;Hristovski et al, 2013;Ganiz et al, 2005), initially not widely adopted outside of computer science (Lardos et al, 2022;Henry & McInnes, 2017), while gaining prominence as of recent (Cesario et al, 2024). Translation into mainstream drug research has primarily been hindered by limited empirical evaluation of new discoveries, lack of communication between biomedical and information systems communities and computationally intense nature of involved methods (Moreau, 2023;Cheerkoot-Jalim & Khedo, 2021;Henry & McInnes, 2017;Lardos et al, 2022;.…”

“…These shortcomings are increasingly being addressed by developers (Cesario et al, 2024;Zong et al, 2022;Henry & McInnes, 2017). Still, LBD drug repurposing pipelines are seldom validated with external EHRs, and if done, only on one single dataset and not sufficiently transparent (Zong et al, 2022;Lardos et al, 2022;Dara et al, 2022;Ji et al, 2020;Cheerkoot-Jalim & Khedo, 2021). In cases where EHRs are utilised, circulation to wider audiences is hindered through limited accessibility and understanding of EHRs (Zong et al, 2022).…”

Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference

“…Multiple approaches have been implemented to formalize the notion of LBD [ 29 ]. In its original conception, Swanson described the ‘ABC model’ for connecting a source research area, A, to a target area, C, via an intermediate area, B, that has overlap with both A and C. In closed LBD, A and C are specified and the task is to explain what connection, B, might relate the concepts.…”

Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph–Based Approach

“…The biomedical literature has abundant information on the molecular virology of coronaviruses [ 13 ]. Existing drug and genetic knowledge bases already contain a wealth of proven biomedical knowledge.…”