Advanced analytics for the automation of medical systematic reviews

While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic reviews. Specifically, we used soft-margin polynomial Support Vector Machine (SVM) as a classifier, exploited Unified Medical Language Systems (UMLS) for medical terms extraction, and examined various techniques to resolve the class imbalance issue. Through an empirical study, we demonstrated that soft-margin polynomial SVM achieves better classification performance than the existing algorithms used in current research, and the performance of the classifier can be further improved by using UMLS to identify medical terms in articles and applying re-sampling methods to resolve the class imbalance issue

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Artificial Intelligence Applied to Supply Chain Management and Logistics: Systematic Literature Review

The growing impact of automation and artificial intelligence (AI) on supply chain management and logistics is remarkable. This technological advance has the potential to significantly transform the handling and transport of goods. The implementation of these technologies has boosted efficiency, predictive capabilities and the simplification of operations. However, it has also raised critical questions about AI-based decision-making. To this end, a systematic literature review was carried out, offering a comprehensive view of this phenomenon, with a specific focus on management. The aim is to provide insights that can guide future research and decision-making in the logistics and supply chain management sectors. Both the articles in this thesis and that form chapters present detailed methodologies and transparent results, reinforcing the credibility of the research for researchers and managers. This contributes to a deeper understanding of the impact of technology on logistics and supply chain management. This research offers valuable information for both academics and professionals in the logistics sector, revealing innovative solutions and strategies made possible by automation. However, continuous development requires vigilance, adaptation, foresight and a rapid problem-solving capacity. This research not only sheds light on the current panorama, but also offers a glimpse into the future of logistics in a world where artificial intelligence is set to prevail

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Active Learning for the Automation of Medical Systematic Review Creation

While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation of these reviews is resource intensive. To mitigate this problem there has been some attempts to leverage supervised machine learning to automate the article triage procedure. This approach has been proved to be helpful for updating existing SRs. However, this technique holds very little promise for creating new SRs because training data is rarely available when it comes to SR creation. In this research we propose an active machine learning approach to overcome this labeling bottleneck and develop a classifier for supporting the creation of systematic reviews. The results indicate that active learning based sample selection could significantly reduce the human effort and is viable technique for automating medical systematic review creation with very few training dataset