Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications. This study presents a novel method for detecting both collective and individual anomalies in healthcare data through time series analysis using unsupervised machine learning. The dual-strategy approach leverages two methodologies: a 'practice centre-based approach' which monitors changes across different practice centres and a 'process-based approach' which focuses on identifying anomalies within individual centres. The former allows for early detection of systemic issues, while the latter highlights specific irregularities within a centre's operations. The study utilised a dataset over 500,000 medical records from multiple GP practice centres in the UK collected between 2018-2023. Data are clustered using DBSCAN to identify collective anomalies from deviations from linear trends in consecutive two-month scatterplots. Individual anomalies are identified by examining the SOM-clustered time series of various medical processes within a specific practice centre, where graphs show deviation from the typical pattern. Our approach addresses some challenges posed by the complexity and sensitivity of healthcare data by not requiring personal information. The method offers accurate visual representations making the data accessible and interpretable for non-technical users. Unlike traditional methods focusing solely on subsequence anomalies, our technique analyses the collective behaviour across multiple time series providing a more comprehensive perspective. This study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector.

Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to identify patients at risk using big data analytics. This will improve patient outcomes by enabling pre-emptive changes to medication on the identified cohort before symptoms present. This paper presents a mean-shift clustering technique used to identify groups of patients at the highest risk of polypharmacy. A weighted anticholinergic risk score and a weighted drug interaction risk score were calculated for each of 300,000 patient records registered with a major regional UK-based healthcare provider. The two measures were input into the mean-shift clustering algorithm and this grouped patients into clusters reflecting different levels of polypharmaceutical risk. Firstly, the results showed that, for most of the data, the average scores are not correlated and, secondly, the high risk outliers have high scores for one measure but not for both. These suggest that any systematic recognition of high-risk groups should consider both anticholinergic and drug–drug interaction risks to avoid missing high-risk patients. The technique was implemented in a healthcare management system and easily and automatically identifies groups at risk far faster than the manual inspection of patient records. This is much less labour-intensive for healthcare professionals who can focus their assessment only on patients within the high-risk group(s), enabling more timely clinical interventions where necessary.

Many undergraduate business students in the United Kingdom (UK) find themselves during the initial stages of their degree programmes required to study numerical subjects which, for many, have not been encountered since completing compulsory education. This paper considers the utilisation of a technology-based approach in order to support first-year undergraduate students engage with and achieve in the business numeracy subject area. The purpose of this paper is providing background introduction to new pedagogy through the use of technology. Through a longitudinal study conducted within a modern university in the UK over a 11-year period (2006–2017), this paper qualifies the impact of a video-based learning (VBL) approach on the achievement profiles of first-year undergraduate students within a modern business school. Results indicate that the use of a VBL approach such as that adopted within this study supports not only improvement in the number of students gaining an overall pass, but also an overall improvement amongst higher achieving students within business numeracy. Findings also demonstrate those characteristics of a VBL approach that support continuation of student engagement with the subject matter throughout their studies. Whilst such findings offer a range of benefits to both students and educators in enhancing student achievement, these also serve to introduce a new learning model which will be explored in subsequent research.

This paper presents SimiMatch, an element based schema matching approach for semi-structured and Linked Data. It contributes towards a virtual data integration system that is able to provide transparent access to heterogeneous and autonomous sources. SimiMatch addresses the challenge of sustaining the continuous changes of a large-scale web of data through the use of semantic similarity measurement. The output is a domain-dependent global schema that is created and updated automatically through an unsupervised process. A test of the implementation and an evaluation of SimiMatch in three domains (movie, geographical and people data) demonstrate the effectiveness and the performance of the approach.

Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated with intraday trading using the EURO/USD currency pair. This time series reduction method focuses on short periods preceding significant movements in financial features and allows the most popular neural network architectures to be applied using less powerful but more readily available computer resources. It is shown that the proposed data preprocessing method for machine learning and other AI‐techniques successfully reduced the size of the selected dataset covering a three‐year period (2018–2021) by 275 times.