Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Abstract

Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

Knowledge Discovery and Hypothesis Generation from Online Patient Forums: A Research Proposal

“…Although the integration of data from different biomedical sources has become a booming topic in recent years (Sacchi and Holmes, 2016), only two studies have cross-linked user-generated content from health-related social media with structured databases. Benton et al (2011) compared co-occurrence of side effects in breast cancer posts to drug package labels, whereas Yeleswarapu et al (2014) combined user comments with structured databases and MEDLINE abstracts to calculate the strength of associations between drugs and their side effects.…”

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

“…The integrated, semi-structured and machine readable nature of KGs provides an ideal basis for the development of sophisticated knowledge discovery and data mining (KDD) applications (Holmes, 2014;Sacchi & Holmes, 2016) . Exploratory data mining (EDM), a sub discipline of knowledge discovery, requires an extensive exploration stage, using both intelligent and intuitive techniques, before predictive modelling and confirmatory analysis can realistically and usefully be applied (De Bie, 2013;De Bie & Spyropoulou, 2013) .…”

“…Compared to more traditional data models, knowledge graphs are very flexible at integrating and 59 searching connected heterogeneous data, where data schemas are not established a-priori (Yoon 60 et al, 2017), and often subject to frequent changes. KGs in various forms have been widely 61 adopted in many disciplines, ranging from social sciences to engineering, physics, computer The integrated, semi-structured and machine readable nature of KGs provides an ideal basis for 67 the development of sophisticated knowledge discovery and data mining (KDD) applications 68 (Holmes, 2014;Sacchi & Holmes, 2016). Exploratory data mining (EDM), a sub discipline of 69 knowledge discovery, requires an extensive exploration stage, using both intelligent and intuitive 70 techniques, before predictive modelling and confirmatory analysis can realistically and usefully be 71 applied (De Bie, 2013; De Bie & Spyropoulou, 2013).…”

The Devices, Experimental Scaffolds, and Biomaterials Ontology (DEB): A Tool for Mapping, Annotation, and Analysis of Biomaterials Data

“…It is also a valuable asset for standardized meta‐data indexing, resource‐linking, and cross‐disciplinary data exploitation. Finally, proper identification of used biomaterials in experiments can improve the adoption of FAIR principals (findable, accessible, interoperable, and reusable data [ 18 ] ) in biomaterials' research and automatic knowledge discovery [ 19 ] in the field.…”