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The utilisation of machine learning techniques to predict Alzheimer's Disease (AD) progression will substantially assist researchers and clinicians in establishing effective AD prevention and treatment strategies. In this research, we present a novel Multi-Task Learning (MTL) model for modelling AD progression based on tensor formation from spatio-temporal similarity measures of brain biomarkers. In this model, each patient sample's prediction in the tensor is assigned to a task, with each task sharing a set of latent factors acquired via tensor decomposition. To further improve the performance of the model, we present a novel regularisation term which utilises the convex combination of disease progression to modify longitudinal stability and ensure that two regression models have a minimal variation at successive time points. The model can be utilised to effectively predict AD progression with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at various stages. We conducted extensive experiments to evaluate the performance for the proposed model and algorithm utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to single-task and state-of-the-art multi-task regression techniques, our proposed method has greater accuracy and stability for predicting AD progress in terms of root mean square error, with an average reduction of 2.60 compared to single-task regression methods and 1.17 compared to multi-task regression methods in the Mini-Mental State Examination (MMSE) questionnaire; with an average reduction of 5.08 compared to single-task regression methods and 2.71 compared to multi-task regression methods in the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).

NIHR (National Institute for Health Research) Devices for Dignity MedTech Cooperative (D4D) and NIHR Children and Young People MedTech Cooperative (CYPMedTech) have established track records in keeping patient and public involvement (PPI) at the core of medical technology development, evaluation and implementation. The 2020 global COVID-19 pandemic presented significant challenges to maintaining this crucial focus. In this paper we describe prior successful methodologies and share examples of the adaptations made in order to continue to engage with patients and the public throughout the pandemic and beyond. We reflect on learning gained from these experiences, and new areas of scope and focus relating to broadening the reach of engagement and representation, along with associated resource requirements and impact metrics.

Machine learning approaches for predicting Alzheimer's disease (AD) progression can substantially assist researchers and clinicians in developing effective AD preventive and treatment strategies. This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects' temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions. We have conducted extensive experiments utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate the performance of the proposed algorithm and model. Results demonstrate that the proposed model have superior accuracy and stability in predicting AD progression compared to benchmarks and state-of-the-art multi-task regression methods in terms of the Mini Mental State Examination (MMSE) questionnaire and The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) cognitive scores. Brain biomarker correlation information can be utilised to identify variations in individual brain structures and the model can be utilised to effectively predict the progression of AD with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages.

Alzheimer's disease (AD) is a typical progressive neurodegenerative disease with insidious onset. Utilising various biomarkers to track and predict AD progression for supporting clinic decisions has recently received wide attentions. Accurate prediction of disease progression will help clinicians and patients make the best decisions on disease prevention and treatment. Typical prediction models focus on extracting biomarker morphological information of different regions of interest (ROIs) from magnetic resonance imaging (MRI) or positron emission tomography (PET), such as the average regional cortical thickness and regional volume. They are effective in modeling AD progression and understanding AD biomarkers, but cannot make full utilise of the internal temporal and spatial relationships between these biomarkers to improve the accuracy and stability of AD prediction. In this paper, we propose a new multi-task learning (MTL) method based on the tensor composed of the spatio-temporal similarity measure between brain biomarkers, using MRI data and cognitive scores of AD patients in different stages can effectively predict the progression of AD. Specifically, we define a temporal and spatial feature similarity measure to calculate the rate of change and velocity of each biomarker in MRI to form a vector, which represents the morphological changing trend of the biomarker, then we calculate the similarity of the changing trend between two biomarkers and encode the data to the third-order tensor, and extract interpretable biomarker latent factors from the original data. The prediction of each patient sample in the tensor is a task and all prediction tasks share a set of latent factors obtained from tensor decomposition to train the AD progression prediction model, which learns task correlation from the spatiotemporal tensor itself. We conducted extensive experiments utilising the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Experimental results show that compared with ROI-based traditional single feature regression methods, our proposed method has better accuracy and stability in disease progression prediction in terms of root mean square error exhibiting an average of 4.10 decrease compared to Ridge regression, 0.19 decrease compared to Lasso regression and 0.18 decrease compared to Temporal Group Lasso (TGL) in the Mini Mental State Examination (MMSE) questionnaire.

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. Methods and analysis: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. Ethics and dissemination: The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. Trial registration number: ISRCTN (12246987). We acknowledge support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. SDD, AY and LRo are also supported by the Newcastle Biomedical Research Centre (BRC) based at Newcastle upon Tyne and Newcastle University. CM, BS, LVG and EB are also supported by the Sheffield Biomedical Research Centre (BRC) based at the Sheffield Teaching Hospital and the University of Sheffield. The work was also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust and the CRF at the Sheffield Teaching Hospital. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care or the funders.This study was co-funded by the European Union’s Horizon 2020 research and innovation programme and EFPIA via the Innovative Medicine Initiative 2 (Mobilise-D project, grant number IMI22017-13-7-820820). The views expressed are those of the authors and not necessarily those of the IMI, the European Union, the EFPIA, or any Associated Partners. We acknowledge the support of Grünenthal GmbH via the funding of a PhD scholarship directly dedicated to the technical validation protocol.

The HeadUp collar (previously known as the Sheffield Support Snood) provides support for neck weakness caused by amyotrophic lateral sclerosis (ALS) and has shown to be superior to alternative options in a small cohort of patients from one single center. Here we report the assessment of the HeadUp collar in a larger cohort of patients, exploring the use in other neurological conditions and expanding to other centers across the UK and Ireland. An interventional cross-sectional study design was implemented to investigate the usability and acceptability of the HeadUp collar. A total of 139 patients were recruited for the study, 117 patients had a diagnosis of ALS and 22 patients presented with neck weakness due to other neurological conditions. Participants were assessed at baseline, fitted a HeadUp collar and followed-up one month later. The performance of the HeadUp collar was rated favorably compared to previously worn collars in terms of the ability to eat, drink and swallow. Findings suggest that the collar also permitted a more acceptable range of head movements whilst maintaining a good level of support. We conclude that the HeadUp collar is a suitable option for patients with neck weakness due to ALS and other neurological conditions.

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.

The care sector requires support and new ways of designing and delivering services to respond to the needs of an increasing ageing population, characterised by significant numbers of people who encounter difficulties in living independently in their own home due to the collective effect of ageing, frailty and overlapping complications of long term conditions. The use of technology in care homes has a role to play as an enabler for new care models. In this chapter we review a selection of digital technologies for integrated care, present two case studies evaluating innovative digital solutions in care homes, and consider the user-centred design principles to aid successful and sustainable implementation.

This chapter presents context for the need for better designed technologies within and outside of healthcare settings to support the increasing needs of ageing populations, and provides a model and exemplars through which technology development can have a greater impact.

We describe a methodology and a technology supporting an intervention carried out by Public Health England (PHE) to encourage physically inactive people (doing less than 30 minutes' physical activity per week) to initiate regular physical activity via 10 minutes of daily brisk walking. The intervention is designed to encourage the inclusion of short bouts of continuous brisk walking in everyday activities such as shopping or commuting. To this extent a behaviour change mobile application, Active 10, was developed and distributed freely for Android and iOS. The app was downloaded over 620,000 times and our server infrastructure has collected nearly a billion data points between March 2017 and January 2019. The paper describes the rationale for Active 10, the application supporting the intervention, the data architecture and the data collection approach. Then we discuss the complexity of developing a health tracking technology with such large number of users, producing a significant volume of data. Finally, we describe a preliminary data analysis, focussing on a cohort of 129,010 users who used the app for over 8 weeks: 73% of these users achieved less than ten minutes of brisk walking per day during the first week; by the end of the 8th week this subset of users showed, on average, a 10-fold increase in brisk walking. The most inactive section of the cohort, the 54% of users who showed virtually no brisk walking activity during week 1, seems to achieve the greatest proportional increase, and by the end of week 8 they appear to meet, on average, 10 minutes of continuous brisk walking per day. The increase is more evident within the 15% of the cohort who kept the app for over six months: on average a 12% increase in average activity was observed in this group with no sign of decline.

The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers.

PurposeThis paper seeks to describe the preliminary studies (on both users and data), the design and evaluation of the K‐Search system for searching legacy documents in aerospace engineering. Real‐world reports of jet engine maintenance challenge the current indexing practice, while real users' tasks require retrieving the information in the proper context. K‐Search is currently in use in Rolls‐Royce plc and has evolved to include other tools for knowledge capture and management.Design/methodology/approachSemantic Web techniques have been used to automatically extract information from the reports while maintaining the original context, allowing a more focused retrieval than with more traditional techniques. The paper combines semantic search with classical information retrieval to increase search effectiveness. An innovative user interface has been designed to take advantage of this hybrid search technique. The interface is designed to allow a flexible and personal approach to searching legacy data.FindingsThe user evaluation showed that the system is effective and well received by users. It also shows that different people look at the same data in different ways and make different use of the same system depending on their individual needs, influenced by their job profile and personal attitude.Research limitations/implicationsThis study focuses on a specific case of an enterprise working in aerospace engineering. Although the findings are likely to be shared with other engineering domains (e.g. mechanical, electronic), the study does not expand the evaluation to different settings.Originality/valueThe study shows how real context of use can provide new and unexpected challenges to researchers and how effective solutions can then be adopted and used in organizations.

The analysis of data using a visual tool is rarely a task done in isolation, it tends to be part of a wider goal: that of making sense of the current situation, often to support decision-making. A user-centred approach is needed in order to properly design interaction that supports sense-making incorporating visual data analysis. This paper reports the experience gained in X-Media, a project that aims to support knowledge management (KM), sharing and reuse across different media in large enterprises. We report the user-centred design approach adopted and the design phases that led to the first prototype. A user evaluation was conducted to assess the design and how different levels of data, information and knowledge were mapped using alternative visual tools. The results show that a clear separation of the visual data analysis from other sense-making sub-tasks helps users in focussing their attention. Users particularly appreciated the data analysis across different media and formats, as well as the support for contextualising information within the broader perspective of KM. Further work is needed to develop more fully intuitive visualisations that exploit the richer information in multimedia documents and make the multiple connections between data more easily accessible.