Applying a novel approach to scoping review incorporating artificial intelligence: mapping the natural history of gonorrhoea.

BACKGROUND: Systematic and scoping literature searches are increasingly resource intensive. We present the results of a scoping review which combines the use of a novel artificial-intelligence-(AI)-assisted Medline search tool with two other 'traditional' literature search methods. We illustrate this novel approach with a case study to identify and map the range of conditions (clinical presentations, complications, coinfections and health problems) associated with gonorrhoea infection. METHODS: To fully characterize the range of health outcomes associated with gonorrhoea, we combined a high yield preliminary search with a traditional systematic search, then supplemented with the output of a novel AI-assisted Medline search tool based on natural language processing methods to identify eligible literature. RESULTS: We identified 189 health conditions associated with gonorrhoea infection of which: 53 were identified through the initial 'high yield' search; 99 through the systematic search; and 124 through the AI-assisted search. These were extracted from 107 unique references and 21 International Statistical Classification of Diseases and Related Health Problems Ninth and Tenth Revision (ICD 9/10) or Read codes. Health conditions were mapped to the urogenital tract (n = 86), anorectal tract (n = 6) oropharyngeal tract (n = 5) and the eye (n = 14); and other conditions such as systemic (n = 61) and neonatal conditions (n = 7), psychosocial associations (n = 3), and co-infections (n = 7). The 107 unique references attained a Scottish Intercollegiate Guidelines Network (SIGN) score of ≥2++ (n = 2), 2+ (14 [13%]), 2- (30 [28%]) and 3 (45 [42%]), respectively. The remaining papers (n = 16) were reviews. CONCLUSIONS: Through AI screening of Medline, we captured - titles, abstracts, case reports and case series related to rare but serious health conditions related to gonorrhoea infection. These outcomes might otherwise have been missed during a systematic search. The AI-assisted search provided a useful addition to traditional/manual literature searches especially when rapid results are required in an exploratory setting.

Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts.

Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., 'Linlog', 'Backbone' and 'sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness.