Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations.

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling methods are widely used due to their simplicity and flexibility. Most existing resampling techniques aim at rebalancing class distribution. However, class imbalance is not the only factor that impacts the performance of the learning algorithm. Class overlap has proved to have a higher impact on the classification of imbalanced datasets than the dominance of the negative class. In this paper, we propose a new undersampling method that eliminates negative instances from the overlapping region and hence improves the visibility of the minority instances. Testing and evaluating the proposed method using 36 public imbalanced datasets showed statistically significant improvements in classification performance.

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders’ concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.