Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Using anticipative hybrid extreme rotation forest to predict emergency service readmission risk

This paper provides a real life application of the recently published Anticipative Hybrid Extreme RotationForest (AHERF), which is an heterogeneous ensemble classifier that anticipates the correct fraction ofinstances from each basic classifier architecture to be included in the ensemble. Heterogeneous classi-fier ensembles aim to profit from the diverse problem domain specificities of each classifier architecturein order to achieve improved generalization over a larger spectrum of problem domains. Given a prob-lem dataset, anticipative determination of the desired ensemble composition is carried out as follows:First, we estimate the performance of each classifier architecture by independent pilot cross-validationexperiments on a small subsample of the data. Next, classifier architectures are ranked according to theiraccuracy results. The likelihood of each classifier architecture instance appearing in the ensemble is com-puted from this ranking. Finally, while building the ensemble, the architecture of each individual classifieris decided by sampling this likelihood probability distribution. In this paper we provide an applicationof AHERF to a real life problem. Readmission of patients short time (i.e. 72 h) after being released poses agreat economical and social challenge, so that many efforts are being addressed to predict and avoid read-mission events. We present the results of the application of AHERF over a real life dataset composed of156,120 admission cases recorded between January 2013 and August 2015. AHERF archives results overor close to 70% sensitivity in the prediction of readmissions for adults and pediatric cases, suggesting thatit can be used to build institution specific prediction systems.Basque Government Grant IT874-13 for the ComputationalIntelligence research group

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Practical approaches to delivering pandemic impacted laboratory teaching

#DryLabsRealScience is a community of practice established to support life science educators with the provision of laboratory-based classes in the face of the COVID-19 pandemic and restricted access to facilities. Four key approaches have emerged from the innovative work shared with the network: videos, simulations, virtual/augmented reality, and datasets, with each having strengths and weaknesses. Each strategy was used pre-COVID and has a sound theoretical underpinning; here, we explore how the pandemic has forced their adaptation and highlight novel utilisation to support student learning in the laboratory environment during the challenges faced by remote and blended teaching

Using Active Learning to Teach Critical and Contextual Studies: One Teaching Plan, Two Experiments, Three Videos.

Since the 1970s, art and design education at UK universities has existedas a divided practice; on the one hand applying active learning in thestudio and on the other hand enforcing passive learning in the lecturetheatre. As a result, art and design students are in their vast majorityreluctant about modules that may require them to think, read and writecritically during their academic studies. This article describes, evaluatesand analyses two individual active learning experiments designed todetermine if it is possible to teach CCS modules in a manner thatencourages student participation. The results reveal that opting foractive learning methods improved academic achievement, encouragedcooperation, and enforced an inclusive classroom. Furthermore, andcontrary to wider perception, the article demonstrates that activelearning methods can be equally beneficial for small-size as well aslarge-size groups

The history of banking technologies in the UK: patterns of technological investment decision-making and expertise

The central role of banking in the 2008 credit crisis has been the source of much controversy about the quality and robustness of decision-making in the Financial Services sector. This paper aims to surface the influence of the historical evolution of expertise in the banking sector, on such decisions and, in so doing, to underline that the decision-making activity is strongly linked to the views of dominant expert groups in the industry in each era. The paper proposes that Technological Investment Decision Making (TIDM), as viewed historically, has been highly contingent to both technological developments in banking and the subsequent developments in banking expertise that provides the pool for decision-makers in the industry.The paper adopts an historical perspective to illustrate that, counter to popular belief, TIDM is a socially constructed process rather than the outcome of any normative exercise. History demonstrates that there is no optimal method for TIDM with rigour and accuracy of execution determining successful outcomes. On the contrary, in each era, the "right way" to perform TIDM has always been underpinned by the standpoints and beliefs of specialised practitioners who dominated the UK banking industry and by the received wisdom of a community of expert professionals, administrators and think tanks, dictating "realities" on the state of the economy, the role of banks and the value of technologies