Clinical decision support system for early detection and diagnosis of dementia

Dementia is a syndrome caused by a chronic or progressive disease of the brain, which affects memory, orientation, thinking, calculation, learning ability and language. Until recently, early diagnosis of dementia was not a high priority, since the related diseases were considered untreatable and irreversible. However, more effective treatments are becoming available, which can slow the progress of dementia if they are used in the early stages of the disease. Therefore, early diagnosis is becoming more important. The Clock Drawing Test (CDT) and Mini Mental State Examination (MMSE) are well-known cognitive assessment tests. A known obstacle to the wider usage of the CDT assessments is the scoring and interpretation of the results. This thesis introduces a novel diagnostic Clinical Decision Support System (CDSS) based on CDT which can help in the diagnosis of three stages of dementia. It also introduces the advanced methods developed for the interpretation and analysis of CDTs. The data used in this research consist of 604 clock drawings produced by dementia patients and healthy individuals. A comprehensive catalogue of 47 visual features within CDT drawings is proposed to enhance the sensitivity of the CDT in diagnosing the early stages of dementia. These features are selected following a comprehensive analysis of the available data and the most common CDT scoring systems reported in the medical literature. These features are used to build a new digitised dataset necessary for training and validating the proposed CDSS. In this thesis, a novel feature selection method is proposed for the study of CDT feature significance and to define the most important features in diagnosing dementia. iii A new framework is also introduced to analyse the temporal changes in the CDT features corresponding to the progress of dementia over time, and to define the first onset symptoms. The proposed CDSS is designed to differentiate between four cognitive function statuses: (i) normal; (ii) mild cognitive impairment or mild dementia; (iii) moderate or severe dementia; and (vi) functional. This represents a new application of the CDT, as it was previously used only to detect the positive dementia cases. Diagnosing mild cognitive impairment or early stage dementia using CDT as a standalone tool is a very challenging task. To address this, a novel cascade classifier is proposed, which benefits from combining CDT and MMSE to enhance the overall performance of the system. The proposed CDSS diagnoses the CDT drawings and places them into one of three cognitive statuses (normal or functional, mild cognitive impairment or mild dementia, and moderate or severe dementia) with an accuracy of 78.34 %. Moreover, the proposed CDSS can distinguish between the normal and the abnormal cases with accuracy of 89.54 %. The achieved results are good and outperform most of CDT scoring systems in discriminating between normal and abnormal cases as reported in existing literature. Moreover, the system shows a good performance in diagnosing the CDT drawings into one of the three cognitive statuses, even comparing well with the performance of dementia specialists. The research has been granted ethical approval from the South East Wales Research Ethics Committee to employ anonymised copies of clock drawings and copies of Mini Mental State Examination made by patients during their examination by the memory team in Llandough hospital, Cardif

Minimal Patients’ Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Background: Stress echocardiography (SE) is a well-established diagnostic tool in assessing patients with suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of SE and patients’ variables including cardiovascular risk factors, current medication and anthropometric variables has not been widely investigated. Objective: This study aims to use Machine Learning (ML) to predict significant CAD defined by positive SE results in patients with chest pain based on patients’ anthropometrics, cardiovascular risk factors and medication as variables. Methods: A ML framework is proposed to automate the prediction of SE results. The proposed framework consists of four stages; feature extraction, pre-processing, feature selection and classification stage. A mutual information-based feature selection method was used to investigate the amount of information that each feature carries to define the positive outcome of SE. Two classification algorithms, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, and Random Forest classifiers have been deployed. Data from 529 patients have been used to train and validate the proposed framework. Their mean age was 61 (±12 SD). The data consists of the anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolaemia, prior diagnosis of CAD and prescribed medications at the time of the test. The results of the SE were defined as outcome. A total of 82 patients had positive (abnormal) and 447 negative (normal) results, respectively. The proposed framework has been evaluated using the whole dataset including the cases with prior diagnosis of CAD. Five folds cross validation was used to validate the performance of the proposed framework. We also investigated the model in the subset of patients with no prior CAD. Results: The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin convertase enzyme inhibitor or angiotensin receptor blocker were the features that shared the most information about the outcome of SE. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. The best trade-off between sensitivity and specificity for the whole dataset accuracy was 66.63% with sensitivity and specificity 72.87%, and 67.67% respectively. However, for patients with no prior diagnosis of CAD only two features (sex and angiotensin convertase enzyme inhibitor or angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. Conclusions: This pilot study shows that ML can predict the outcome of SE in detecting significant CAD based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent SE could further improve the performance of the proposed algorithm with the potential of facilitating patient’s selection for early treatment / intervention with avoiding un-necessary downstream testing

A Nonproprietary Movement Analysis System (MoJoXlab) Based on Wearable Inertial Measurement Units Applicable to Healthy Participants and Those With Anterior Cruciate Ligament Reconstruction Across a Range of Complex Tasks: Validation Study

Background: Movement analysis in a clinical setting is frequently restricted to observational methods to inform clinical decision making, which has limited accuracy. Fixed-site, optical, expensive movement analysis laboratories provide gold standard kinematic measurements; however, they are rarely accessed for routine clinical use. Wearable inertial measurement units (IMUs) have been demonstrated as comparable, inexpensive, and portable movement analysis toolkits. MoJoXlab has therefore been developed to work with generic wearable IMUs. However, before using MoJoXlab in clinical practice, there is a need to establish its validity in participants with and without knee conditions across a range of tasks with varying complexity. Objective: This paper aimed to present the validation of MoJoXlab software for using generic wearable IMUs for calculating hip, knee, and ankle joint angle measurements in the sagittal, frontal, and transverse planes for walking, squatting, and jumping in healthy participants and those with anterior cruciate ligament (ACL) reconstruction. Methods: Movement data were collected from 27 healthy participants and 20 participants with ACL reconstruction. In each case, the participants wore seven MTw2 IMUs (Xsens Technologies) to monitor their movement in walking, jumping, and squatting tasks. The hip, knee, and ankle joint angles were calculated in the sagittal, frontal, and transverse planes using two different software packages: Xsens’ validated proprietary MVN Analyze and MoJoXlab. The results were validated by comparing the generated waveforms, cross-correlation (CC), and normalized root mean square error (NRMSE) values. Results: Across all joints and activities, for data of both healthy and ACL reconstruction participants, the CC and NRMSE values for the sagittal plane are 0.99 (SD 0.01) and 0.042 (SD 0.025); 0.88 (SD 0.048) and 0.18 (SD 0.078) for the frontal plane; and 0.85 (SD 0.027) and 0.23 (SD 0.065) for the transverse plane (hip and knee joints only). On comparing the results from the two different software systems, the sagittal plane was very highly correlated, with frontal and transverse planes showing strong correlation. Conclusions: This study demonstrates that nonproprietary software such as MoJoXlab can accurately calculate joint angles for movement analysis applications comparable with proprietary software for walking, squatting, and jumping in healthy individuals and those following ACL reconstruction. MoJoXlab can be used with generic wearable IMUs that can provide clinicians accurate objective data when assessing patients’ movement, even when changes are too small to be observed visually. The availability of easy-to-setup, nonproprietary software for calibration, data collection, and joint angle calculation has the potential to increase the adoption of wearable IMU sensors in clinical practice, as well as in free living conditions, and may provide wider access to accurate, objective assessment of patients’ progress over time

Huntington's Disease assessment using tri axis accelerometers

Huntington’s disease (HD) is a progressive inherited neurodegenerative disorder, causing involuntary movement and cognitive problems, severely affecting the quality of life. Controlling upper limb function is a core feature of daily activity and can prove problematic for people with HD. The Money Box Test (MBT) has been developed with a purpose of quantifying the involuntary movement frequently seen in people with HD. In this research, wearable and highly sensitive accelerometers are used to collect the acceleration of the hands and chest during the performance of the MBT. Using this data, a new approach is proposed to automatically classify the participants into two classes, healthy and HD, on the basis of the time series accelerometer data. A set of 90 time domain features is extracted from the accelerometer data, a feature selection technique is used to analyse the feature significance and to reduce the dimensionality of the dataset, and finally an SVM classifier is used to classify subjects into healthy and HD classes. The data of seven healthy controls and 15 HD patients are used in this study. The highest accuracy with the most significant eight features is 86.36% with the sensitivity and the specificity values being 87.50%, and 83.33% respectively

Digital Intervention in Loneliness in Older Adults: Qualitative Analysis of User Studies

Background: Loneliness is a significant well-being issue that affects older adults. Existing, commonly used social connection platforms do not contain facilities to break the cognitive cycle of loneliness, and loneliness interventions implemented without due processes could have detrimental effects on well-being. There is also a lack of digital technology designed with older adults.Objective:We aimed to iteratively design a user-centered smartphone app that can address loneliness in older adults. The aim of this study was to investigate the loneliness-related psychological processes that our conceptual smartphone app promotes. We also identified the emergent needs and concerns that older adults raised regarding the potential benefits and detriments of the app.Methods: We used technology probes to elicit older adults' reflections on the concept of using the app in 2 studies as follows: concept focus groups (n=33) and concept interviews (n=10). We then conducted a prototype trial with 1 week of use and follow-up interviews (n=12).Results: Thematic analysis explored the experiences and emergent challenges of our app through the design process. This led to the development of 4 themes as follows occurring in all 3 qualitative data sets: reflection on a digital social map is reassuring; app features encourage socializing; the risk of compounding loneliness; and individuals feel more control with mutual, socially beneficial activities.Conclusions: Smartphone apps have the potential to increase older adults' awareness of the richness of their social connections, which may support loneliness reduction. Our qualitative approach to app design enabled the inclusion of older adults' experiences in technology design. Thus, we conclude that the older adults in our study most desired functionalities that can support mutual activities and maintain or find new connections rather than enable them to share an emotional state. They were wary of the app replacing their preferred in-person social interaction. Participants also raised concerns about making the user aware of the lack of support in their social network and wanted specific means of addressing their needs. Further user-centered design work could identify how the app can support mutual activities and socializing

Socio-Technical Resilience for Community Healthcare

Older adults at home frequently rely on ‘circles of support’ which range from relatives and neighbours, to the voluntary sector, social workers, paid carers, and medical professionals. Creating, maintaining, and coordinating these circles of support has often been done manually and in an ad hoc manner. We argue that a socio-technical system that assists in creating, maintaining, and coordinating circles of support is a key enabler of community healthcare for older adults. In this paper we propose a framework called SERVICE (SocioTechnical Resilience for the Vulnerable) to help represent, reason about, and coordinate these circles of support and strengthen their capacity to deal with variations in care needs and environment. The objective is to make these circles resilient to changes in the needs and circumstances of older adults. Early results show that older adults appreciate the ability to represent and reflect on their circle of support

Socio-Technical Resilience for Community Healthcare

Older adults at home frequently rely on ‘circles of support’ which range from relatives and neighbours, to the voluntary sector, social workers, paid carers, and medical professionals. Creating, maintaining, and coordinating these circles of support has often been done manually and in an ad hoc manner. We argue that a socio-technical system that assists in creating, maintaining, and coordinating circles of support is a key enabler of community healthcare for older adults. In this paper we propose a framework called SERVICE (Socio-Technical Resilience for the Vulnerable) to help represent, reason about, and coordinate these circles of support and strengthen their capacity to deal with variations in care needs and environment. The objective is to make these circles resilient to changes in the needs and circumstances of older adults. Early results show that older adults appreciate the ability to represent and reflect on their circle of support

Objectively characterizing Huntington's disease using a novel upper limb dexterity test.

Background:The Clinch Token Transfer Test (C3t) is a bi-manual coin transfer task that incorporates cognitive tasks to add complexity. This study explored the concurrent and convergent validity of the C3t as a simple, objective assessment of impairment that is reflective of disease severity in Huntington's, that is not reliant on clinical expertise for administration. Methods:One-hundred-and-five participants presenting with pre-manifest (n = 16) or manifest (TFC-Stage-1 n = 39; TFC-Stage-2 n = 43; TFC-Stage-3 n = 7) Huntington's disease completed the Unified Huntington's Disease Rating Scale and the C3t at baseline. Of these, thirty-three were followed up after 12 months. Regression was used to estimate baseline individual and composite clinical scores (including cognitive, motor, and functional ability) using baseline C3t scores. Correlations between C3t and clinical scores were assessed using Spearman's R and visually inspected in relation to disease severity using scatterplots. Effect size over 12 months provided an indication of longitudinal behaviour of the C3t in relation to clinical measures.Results: Baseline C3t scores predicted baseline clinical scores to within 9-13% accuracy, being associated with individual and composite clinical scores. Changes in C3t scores over 12 months were small ([Formula: see text] ≤ 0.15) and mirrored the change in clinical scores. Conclusion: The C3t demonstrates promise as a simple, easy to administer, objective outcome measure capable of predicting impairment that is reflective of Huntington's disease severity and offers a viable solution to support remote clinical monitoring. It may also offer utility as a screening tool for recruitment to clinical trials given preliminary indications of association with the prognostic index normed for Huntington's disease

Feature selection using Joint Mutual Information Maximisation

Feature selection is used in many application areas relevant to expert and intelligent systems, such as data mining and machine learning, image processing, anomaly detection, bioinformatics and natural language processing. Feature selection based on information theory is a popular approach due its computational efficiency, scalability in terms of the dataset dimensionality, and independence from the classifier. Common drawbacks of this approach are the lack of information about the interaction between the features and the classifier, and the selection of redundant and irrelevant features. The latter is due to the limitations of the employed goal functions leading to overestimation of the feature significance. To address this problem, this article introduces two new nonlinear feature selection methods, namely Joint Mutual Information Maximisation (JMIM) and Normalised Joint Mutual Information Maximisation (NJMIM); both these methods use mutual information and the ‘maximum of the minimum’ criterion, which alleviates the problem of overestimation of the feature significance as demonstrated both theoretically and experimentally. The proposed methods are compared using eleven publically available datasets with five competing methods. The results demonstrate that the JMIM method outperforms the other methods on most tested public datasets, reducing the relative average classification error by almost 6% in comparison to the next best performing method. The statistical significance of the results is confirmed by the ANOVA test. Moreover, this method produces the best trade-off between accuracy and stabilit