Human Evaluation Experiment of Legal Information Retrieval Methods

In this article, I present the results of the human evaluation experiment of three commonly used methods in legal information retrieval and a new “multilayered” approach. I use the doc2vec model, citation network analysis and two topic modelling algorithms for the Czech Supreme Court decisions retrieval and evaluate their performance. To improve the accuracy of the results of these methods, I combine the methods in a “multilayered” way and perform the subsequent evaluation. Both evaluation experiments are conducted with a group of legal experts to assess the applicability and usability of the methods for legal information retrieval. The combination of the doc2vec and citations is found satisfactory accurate for practical use for the Czech court decisions retrieval.

Examining non-technical skills for ad hoc resuscitation teams: a scoping review and taxonomy of team-related concepts

Background Non-technical skills (NTS) concepts from high-risk industries such as aviation have been enthusiastically applied to medical teams for decades. Yet it remains unclear whether—and how—these concepts impact resuscitation team performance. In the context of ad hoc teams in prehospital, emergency department, and trauma domains, even less is known about their relevance and impact. Methods This scoping review, guided by PRISMA-ScR and Arksey & O’Malley’s framework, included a systematic search across five databases, followed by article selection and extracting and synthesizing data. Articles were eligible for inclusion if they pertained to NTS for resuscitation teams performing in prehospital, emergency department, or trauma settings. Articles were subjected to descriptive analysis, coherence analysis, and citation network analysis. Results Sixty-one articles were included. Descriptive analysis identified fourteen unique non-technical skills. Coherence analysis revealed inconsistencies in both definition and measurement of various NTS constructs, while citation network analysis suggests parallel, disconnected scholarly conversations that foster discordance in their operationalization across domains. To reconcile these inconsistencies, we offer a taxonomy of non-technical skills for ad hoc resuscitation teams. Conclusion This scoping review presents a vigorous investigation into the literature pertaining to how NTS influence optimal resuscitation performance for ad hoc prehospital, emergency department, and trauma teams. Our proposed taxonomy offers a coherent foundation and shared vocabulary for future research and education efforts. Finally, we identify important limitations regarding the traditional measurement of NTS, which constrain our understanding of how and why these concepts support optimal performance in team resuscitation. Graphical abstract

Study on Horizon Scanning by Citation Network Analysis and Text Mining: A Focus on Drug Development Related to T Cell Immune Response

Certain innovative technologies applied to medical product development require novel evaluation approaches and/or regulations. Horizon scanning for such technologies will help regulators prepare, allowing earlier access to the product for patients and an improved benefit/risk ratio. This study investigates whether citation network analysis and text mining of scientific papers could be a tool for horizon scanning in the field of immunology, which has developed over a long period, and attempts to grasp the latest research trends. As the result of the analysis, the academic landscape of the immunology field was identified by classifying 90,450 papers (obtained from PubMED) containing the keyword “immune* and t lymph*” into 38 clusters. The clustering was indicative of the research landscape of the immunology field. To confirm this, immune checkpoint inhibitors were used as a retrospective test topic of therapeutics with new mechanisms of action. Retrospective clustering around immune checkpoint inhibitors was found, supporting this approach. The analysis of the research trends over the last 3 to 5 years in this field revealed several candidate topics, including ARID1A gene mutation, CD300e, and tissue resident memory T cells, which shows notable progress and should be monitored for future possible product development. Our results have demonstrated the possibility that citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of life science fields such as immunology.

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords “conventional,” “machine-learning,” or “deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing “young” clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.