BackgroundElectronic patient-reported outcome (ePRO) systems are increasingly used in clinical trials to provide evidence of efficacy and tolerability of treatment from the patient perspective. The aim of this study is twofold: (1) to describe how we developed an electronic platform for patients to report their symptoms, and (2) to develop and undertake usability testing of an ePRO solution for use in a study of cell therapy seeking to provide early evidence of efficacy and tolerability of treatment and test the feasibility of the system for use in later phase studies.MethodsAn ePRO system was designed to be used in a single arm, multi-centre, phase II basket trial investigating the safety and activity of the use of ORBCEL-C™ in the treatment of patients with inflammatory conditions. ORBCEL-C™ is an enriched Mesenchymal Stromal Cells product isolated from human umbilical cord tissue using CD362+ cell selection. Usability testing sessions were conducted using cognitive interviews and the ‘Think Aloud’ method with patient advisory group members and Research Nurses to assess the usability of the system.ResultsNine patient partners and seven research nurses took part in one usability testing session. Measures of fatigue and health-related quality of life, the PRO-CTCAE™ and FACT-GP5 global tolerability question were included in the ePRO system. Alert notifications to the clinical team were triggered by PRO-CTCAE™ and FACT-GP5 scores. Patient participants liked the simplicity and responsiveness of the patient-facing app. Two patients were unable to complete the testing session, due to technical issues. Research Nurses suggested minor modifications to improve functionality and the layout of the clinician dashboard and the training materials.ConclusionBy testing the effectiveness, efficiency, and satisfaction of our novel ePRO system (PROmicsR), we learnt that most people with an inflammatory condition found it easy to report their symptoms using an app on their own device. Their experiences using the PROmicsR ePRO system within a trial environment will be further explored in our upcoming feasibility testing. Research nurses were also positive and found the clinical dashboard easy-to-use. Using ePROs in early phase trials is important in order to provide evidence of therapeutic responses and tolerability, increase the evidence based, and inform methodology development.Trial registration: ISRCTN, ISRCTN80103507. Registered 01 April 2022, https://www.isrctn.com/ISRCTN80103507

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.

Neurologists managing women with Multiple Sclerosis (MS) need information about the safety of disease modifying drugs (DMDs) during pregnancy. However, this knowledge is limited. The present study aims to summarize previous studies by performing a systematic review and meta-analyses. The terms “multiple sclerosis” combined with DMDs of interest and a broad profile for pregnancy terms were used to search Embase and Medline databases to identify relevant studies published from January 2000 to July 2019.1260 studies were identified and ten studies met our inclusion criteria. Pooled risk ratios (RR) of pregnancy and birth outcomes in pregnancies exposed to DMDs compared to those not exposed were calculated using a random effects model. For spontaneous abortion RR = 1.14, 95% CI 0.99–1.32, for preterm births RR = 0.93, 95% CI 0.72–1.21 and for major congenital malformations RR = 0.86, 95% CI 0.47–1.56. The most common major congenital malformations reported in MS patients exposed to MS drugs were atrial septal defect (ASD) (N = 4), polydactyly (N = 4) and club foot (N = 3), which are among the most prevalent birth defects observed in the general population. In conclusion, interferons, glatiramer acetate or natalizumab, do not appear to increase the risk for spontaneous abortions, pre-term birth or major congenital malformations. There were very few patients included that were exposed to fingolimod, azathioprine and rituximab; therefore, these results cannot be generalized across drugs. Future studies including internal comparators are needed to enable treating physicians and their patients to decide on the best treatment options.

We examine how robotic process automation (RPA) can be deployed as a technology solution in business processes and services as a transformation lever beyond traditional business process management and outsourcing. RPA is a new breed of software that allows for enterprise-wide automation of repetitive business processes, with the promise of potential FTE savings, better service quality and improved service delivery. Through an extensive survey of practitioners involved with RPA, this exploratory research identifies the motivation and challenges of RPA implementation as envisaged by the user communities. We also find the complementary perspectives of RPA product development communities. Consequently, we recommend ways to align the perspectives of the user communities and the developer communities to enable definitive value creation for the organisations. The findings and recommendations would help practitioners who develop RPA products or adopt these products in their IT ecosystems.

The emergence of new service oriented distributed models has raised a number of challenges particularly in relation to the management of distributed infrastructures in dynamic environments, such as the Cloud with changing availability of resources, services and quality of services. In such an environment it is very important that users and applications have some level of assurance that their requirements can be satisfied while trying to optimize the usage of the available resources. This paper presents a new requirement-driven decision making mechanism that is based on a quality assured load balancer for distributed computing systems. We evaluate the approach and demonstrate how it can adapt to user requirements and to the capacity of available resources.

Very few topics in today’s digital conversations are more en vogue than Artificial Intelligence and Robotics. However, all too often, debate is centred on the machine and not the human side of this rapidly evolving handshake. This paper introduces a framework that structures the relationship from demystifying through to designing and adopting the handshake, and explores impact on enterprises and provides a future outlook.

We study how contract element extraction can be automated. We provide a labeled dataset with gold contract element annotations, along with an unlabeled dataset of contracts that can be used to pre-train word embeddings. Both datasets are provided in an encoded form to bypass privacy issues. We describe and experimentally compare several contract element extraction methods that use manually written rules and linear classifiers (logistic regression, SVMs) with hand-crafted features, word embeddings, and part-of-speech tag embeddings. The best results are obtained by a hybrid method that combines machine learning (with hand-crafted features and embeddings) and manually written post-processing rules.

As technology moves from being an enabler to become the lifeblood of businesses in the digital era, so does the data used to support the implementation of these technological shifts. In order for the data to be an asset and not a liability, it is primordial to ensure that the data captured and maintained over time is accurate, traceable, reliable and current. However, very often, in order to complete a business function the data from one system may be exported to another system or to a third party tool, which also changes the data. In order to keep track of the data and maximize the use and analysis of data, the Zero Deviation Life Cycle (ZDLC) framework proposes a series of tools to which can trace the data lineage, across several database technologies.

The paper investigates the potential for level 2 autonomous vehicle technology to improve four prevailing sustainability issues specifically on highways: high congestion levels, increasing accident rates, high carbon dioxide emissions and poor journey time reliability. Cooperative adaptive cruise control shows potential to achieve high-volume cooperative driving on highways by controlling these parameters and forming vehicle platoons. Accident rates, carbon dioxide emissions and journey times can be reduced as a result. The risks of platooning are discussed and a minimum safe platoon headway is established to mitigate the risk of vehicle platoon collisions. This headway is applied to a real highway case study, demonstrating the potential to increase notional highway design capacity from 3600 vehicles per hour (vph) to 9213 vph, with significant sustainability improvements possible. Recommendations are made to complete a number of policy implementation and technology development tasks aimed to create the best chance of achieving the identified sustainability benefits within a 20-year timeframe.

The paper investigates the potential for level 2 autonomous vehicle technology to improve four prevailing sustainability issues specifically on highways: high congestion levels, increasing accident rates, high carbon dioxide emissions and poor journey time reliability. Cooperative adaptive cruise control shows potential to achieve high-volume cooperative driving on highways by controlling these parameters and forming vehicle platoons. Accident rates, carbon dioxide emissions and journey times can be reduced as a result. The risks of platooning are discussed and a minimum safe platoon headway is established to mitigate the risk of vehicle platoon collisions. This headway is applied to a real highway case study, demonstrating the potential to increase notional highway design capacity from 3600 vehicles per hour (vph) to 9213 vph, with significant sustainability improvements possible. Recommendations are made to complete a number of policy implementation and technology development tasks aimed to create the b...