Data science for health-care: Patient condition recognition

>Magister Scientiae - MScThe emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) have elicited increased interest in many areas of our daily lives. These include health, agriculture, aviation, manufacturing, cities management and many others. In the health sector, portable vital sign monitoring devices are being developed using the IoT technology to collect patients’ vital signs in real-time. The vital sign data acquired by wearable devices is quantitative and machine learning techniques can be applied to find hidden patterns in the dataset and help the medical practitioner with decision making. There are about 30000 diseases known to man and no human being can possibly remember all of them, their relations to other diseases, their symptoms and whether the symptoms exhibited by the patients are early warnings of a fatal disease. In light of this, Medical Decision Support Systems (MDSS) can provide assistance in making these crucial assessments. In most decision support systems factors a ect each other; they can be contradictory, competitive, and complementary. All these factors contribute to the overall decision and have di erent degrees of influence [85]. However, while there is more need for automated processes to improve the health-care sector, most of MDSS and the associated devices are still under clinical trials. This thesis revisits cyber physical health systems (CPHS) with the objective of designing and implementing a data analytics platform that provides patient condition monitoring services in terms of patient prioritisation and disease identification [1]. Di erent machine learning algorithms are investigated by the platform as potential candidate for achieving patient prioritisation. These include multiple linear regression, multiple logistic regression, classification and regression decision trees, single hidden layer neural networks and deep neural networks. Graph theory concepts are used to design and implement disease identification. The data analytics platform analyses data from biomedical sensors and other descriptive data provided by the patients (this can be recent data or historical data) stored in a cloud which can be private local health Information organisation (LHIO) or belonging to a regional health information organisation (RHIO). Users of the data analytics platform consisting of medical practitioners and patients are assumed to interact with the platform through cities’ pharmacies , rural E-Health kiosks end user applications

Key body pose detection and movement assessment of fitness performances

Motion segmentation plays an important role in human motion analysis. Understanding the intrinsic features of human activities represents a challenge for modern science. Current solutions usually involve computationally demanding processing and achieve the best results using expensive, intrusive motion capture devices. In this thesis, research has been carried out to develop a series of methods for affordable and effective human motion assessment in the context of stand-up physical exercises. The objective of the research was to tackle the needs for an autonomous system that could be deployed in nursing homes or elderly people's houses, as well as rehabilitation of high profile sport performers. Firstly, it has to be designed so that instructions on physical exercises, especially in the case of elderly people, can be delivered in an understandable way. Secondly, it has to deal with the problem that some individuals may find it difficult to keep up with the programme due to physical impediments. They may also be discouraged because the activities are not stimulating or the instructions are hard to follow. In this thesis, a series of methods for automatic assessment production, as a combination of worded feedback and motion visualisation, is presented. The methods comprise two major steps. First, a series of key body poses are identified upon a model built by a multi-class classifier from a set of frame-wise features extracted from the motion data. Second, motion alignment (or synchronisation) with a reference performance (the tutor) is established in order to produce a second assessment model. Numerical assessment, first, and textual feedback, after, are delivered to the user along with a 3D skeletal animation to enrich the assessment experience. This animation is produced after the demonstration of the expert is transformed to the current level of performance of the user, in order to help encourage them to engage with the programme. The key body pose identification stage follows a two-step approach: first, the principal components of the input motion data are calculated in order to reduce the dimensionality of the input. Then, candidates of key body poses are inferred using multi-class, supervised machine learning techniques from a set of training samples. Finally, cluster analysis is used to refine the result. Key body pose identification is guaranteed to be invariant to the repetitiveness and symmetry of the performance. Results show the effectiveness of the proposed approach by comparing it against Dynamic Time Warping and Hierarchical Aligned Cluster Analysis. The synchronisation sub-system takes advantage of the cyclic nature of the stretches that are part of the stand-up exercises subject to study in order to remove out-of-sequence identified key body poses (i.e., false positives). Two approaches are considered for performing cycle analysis: a sequential, trivial algorithm and a proposed Genetic Algorithm, with and without prior knowledge on cyclic sequence patterns. These two approaches are compared and the Genetic Algorithm with prior knowledge shows a lower rate of false positives, but also a higher false negative rate. The GAs are also evaluated with randomly generated periodic string sequences. The automatic assessment follows a similar approach to that of key body pose identification. A multi-class, multi-target machine learning classifier is trained with features extracted from previous motion alignment. The inferred numerical assessment levels (one per identified key body pose and involved body joint) are translated into human-understandable language via a highly-customisable, context-free grammar. Finally, visual feedback is produced in the form of a synchronised skeletal animation of both the user's performance and the tutor's. If the user's performance is well below a standard then an affine offset transformation of the skeletal motion data series to an in-between performance is performed, in order to prevent dis-encouragement from the user and still provide a reference for improvement. At the end of this thesis, a study of the limitations of the methods in real circumstances is explored. Issues like the gimbal lock in the angular motion data, lack of accuracy of the motion capture system and the escalation of the training set are discussed. Finally, some conclusions are drawn and future work is discussed

A Participatory Action Research (PAR) into how the language and vocabulary of diabetes facilitates peoples’ experience of living with diabetes: the Language in Diabetes Study (LIDAS)

There are 4.2 million people diagnosed with diabetes in the UK. It has been established that diabetes causes psychological strain for people with diabetes (PWD) both in increased mental health diagnoses and specific issues under the heading “diabetes distress” (DD). The language and vocabulary of diabetes has been implicated in DD as it may comprise of a restricted code/dialect with negative connotations. Suggestions have also been made to alter speech forms to avoid this utilising alternative vocabulary. A further question however, is why this phenomenon persists at all? Aims: The purpose of the Language in Diabetes Action Study (LIDAS) was to explore how the language and vocabulary of diabetes facilitates peoples’ experience of living with diabetes. As a long-term condition diabetes has a high burden of selfmanagement practices by the patient. Language and vocabulary is seen as a mediating factor in PWD understanding the meaning and purpose of those practices and how they mitigate risks for future complications and promote health. A phenomenological and existential underpinning understands that language conveys meaning but also shapes meaning and this will influence a PWD way of being with diabetes. A thorough review of the literature comprises scientific diabetes-related literature, health psychology literature, existential and post-modernist literature and comparison with language and its significance in other long-term conditions. Method: The method deployed was Participatory Action Research by forming a Cooperative Enquiry group of Co-researchers that participated through cycles of dialogue and reflection to provide a “slice through” rather than a “snapshot” of their lifeworld experience with diabetes. This was in order to move closer to research with PWD as opposed to on PWD. Challenges in study realisation were explored and how technology in terms of video conferencing and transcription assisted. Nine participants contributed in the PAR group to varying degrees, of which a core group of 4 committed Co-researchers provided substantiate involvement over eight months of cycles of reflection and dialogue on a weekly or fortnightly basis by consensus. Additionally, work peers from Diabetes UK volunteered involvement. Co-researchers and team peers provided 27 hours of transcribed material, 9000 words of dialectic material from the DUK Forum, with a total number of participatory voices totalling 40 respondents. The gender of participants, including those from the DUK Forum was quite even (47% male, 53% female). However, female participants contributed more cycles of dialogue and reflection over time. The data then comprises closer to 75% of the transcribed data set. Participants’ age ranged from 21 to 50 with a mean calculated age of 32. A hermeneutic developed for Co-researchers through cycles as they became more invested and involved; this was augmented using interpretive analysis in the manner of Foucauldian genealogy considering bio-power, regimes of truth and Heidegger’s notion of entanglement explicated in his discussion in the question concerning technology. In addition to Co-researchers reflections each dialogue was transcribed and analysed for themes and made available to Co-researchers for further comment, so each cycle refined and reworked the thematic analysis. Findings and Discussion: The LIDAS group findings strongly support the phenomenon of a restricted code/dialect in diabetes and the significance in the aetiology of DD. Furthermore, the findings explore the underlying beliefs implicated and the mechanisms that may open understanding to the root of this phenomenon and the route by which it sustains itself. This abstract, idealistic notion is a discourse that concerns an unreflected belief that a PWD attitude and personality is central to management, termed Capacity to Control. In this way a PWD is viewed as having to become highly motivated, disciplined, ascetic and compliant to a restricted regimen, subjected to measurement and assessment of their HbA1c targets. This is as opposed to attributing diabetes management to Skills, Knowledge and Tools that are contextually optimised for the individual PWD and contribute to their pursuit of lifeworld goals. This is epitomised in the exemplars of Structured Patient Education, Pump Therapy and Flash Glucose Monitoring. The discussion explores how this entangled discourse leads to an illusion of choice, an abstract rather than humanistic view of PWD, burnout and DD, moralism and stigma. The study further notes that tensions over targets, resources and cost produce consumerist healthcare and a notion of a patient’s responsibility to be compliant and that these pressures are the root of Capacity to Control. Synthesis of Findings: This section continues by drawing together the themes revealed in the findings with the current literature and the concepts of Heidegger and Foucault. It also explicates the tensions and forces at play for PWD and HCP, drawing attention to Healthcare for long term conditions viewed in the light of consumerism and moralism. The involvement of counselling psychology and psychotherapy is explored and problems that arise in how referrals are made and what psychological care means for diabetes. Conclusion: The study concludes by assessing study limitations and evaluating PAR as a means of generating data and what this means for the findings. It goes on to explore the gender bias and its possible significance for understanding the findings. An exploration of the possible uses of the findings in relation to HCP training and reflective practice, counselling and psychotherapy services for PWD, patient empowerment for PWD and possible future purposes for PAR in health and psychology. It also suggests possibilities for further research: the potential for using the LIDAS study themes to generate survey questions and establish the extent of applicability with wider participation or to address the gender bias. It also assess the possibility of developing LIDAS adapted patient education for clinical trial compared with current patient education

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

1975/1976 UCI General Catalogue

General catalogue for the academic year 1975-1976

Continuous Glucose Monitoring for the diagnosis of Gestational Diabetes Mellitus.

Gestational Diabetes Mellitus (GDM) incidence and negative outcomes are increasing worldwide. The validity of the oral glucose tolerance test (OGTT) for GDM diagnosis remains contested. Continuous Glucose Monitoring (CGM) could represent a more acceptable and replicable test. Aim of this project was to assess CGM for GDM diagnosis. This PhD thesis is based on five projects: a systematic review of the diagnostic indicators of GDM, an online questionnaire to recruit women at high and low risk of GDM, a retrospective cohort study on the use of the Medtronic iPro2 CGM device for GDM diagnosis, a prospective cohort study on the use of the Abbott Freestyle Libre PRO 2 CGM and a survey study on women and healthcare providers perception of both methods. CGM data were analysed as distribution parameters (mean, CV, SD, maximum value), variability parameters (MAGE and MODD) and time spent in the recommended range, then combined in a CGM score of Variability (CGMSV). In the systematic review were included 174 full-text articles on blood, ultrasound, post-natal and amniotic fluid biomarkers. The ultrasound gestational diabetic score (UGDS) was the most promising biomarker for triangulation. In the GDM risk questionnaire (n=45), triangulation of a composite risk factors score (RFS) with CGMSV and OGTT results highlighted six possible OGTT misdiagnoses (discordant with RFS and CGMSV). In the Medtronic pilot Study (n=73), GDM women (n=33) had significantly higher RFS and CGMSV. The triangulation analysis (n=60) suggested 12 probable misdiagnoses. In the Abbott pilot study (n=87), no significant demographic nor CGM data difference was found between NGT and GDM, possibly due to the small GDM sample size (n=13). With triangulation, 11 OGTT results were potentially false. UGDS (n=22) was positive in only one woman, considered a true negative otherwise. In the survey study, women reported significantly higher acceptability of CGM versus OGTT (n=70 and n=60, respectively), and 94% would recommend CGM for GDM diagnosis. HCP (n=30) scored CGM acceptability significantly lower than women and expressed doubts about the correlation between CGM data and perinatal outcomes. CGM represents a more acceptable alternative to OGTT for GDM diagnosis. HCP expressed doubt about CGM accuracy, and issues of establishing superiority to OGTT remain. Further research on larger cohorts of patients with additional triangulation elements is needed to confirm CGM acceptability and accuracy and refine its use

1974/1975 UCI General Catalogue

General catalogue for the academic year 1974-1975