Challenges in designing an online healthcare platform for personalised patient analytics

The growing number and size of clinical medical records (CMRs) represents new opportunities for finding meaningful patterns and patient treatment pathways while at the same time presenting a huge challenge for clinicians. Indeed, CMR repositories share many characteristics of the classical ‘big data’ problem, requiring specialised expertise for data management, extraction, and modelling. In order to help clinicians make better use of their time to process data, they will need more adequate data processing and analytical tools, beyond the capabilities offered by existing general purpose database management systems or database servers. One modelling technique that can readily benefit from the availability of big data, yet remains relatively unexplored is personalised analytics where a model is built for each patient. In this paper, we present a strategy for designing a secure healthcare platform for personalised analytics by focusing on three aspects: (1) data representation, (2) data privacy and security, and (3) personalised analytics enabled by machine learning algorithms

Analyzing Radicalization and Terrorism: A Situational Action Theory

Although the years since 9/11 have seen an significant increase in the contribution of criminologists to the study of terrorist events, efforts to apply major criminological theories to the understanding of the development of terrorist criminality and individual involvement in terrorist action have lagged. In this chapter, we apply a recently formulated theory of moral action and crime causation, Situational Action Theory, to the explanation of terrorism and radicalization. The case is made that explanations of terrorism and radicalisation should be mechanism-based and integrate all levels of analysis. Situational Action Theory is introduced and examples of its application to the study of terrorism and radicalization are provided. The priorities of a SAT-driven, systematic research agenda are outlined

A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set

Preferred social support roles and methods of communication in college students when presented with potentially anxiety-inducing interpersonal situations

Person by situation, or interactional, psychology predicts that an individual\u27s anxiety will vary across situations. Past studies noted that anxiety increased the likelihood that an individual sought others for social support. Anxiety also affected the method of communication used by individuals. According to the richness model of anxiety, higher anxiety scores are associated with indirect or low richness methods of communication and lower anxiety scores are associated with direct or high richness methods of communication. This study used a person by situation approach to examine both whom participants sought and the method of communication used when presented with potentially anxiety-inducing interpersonal situations. Participants took part in a survey that consisted of a revised Taylor\u27s Manifest Anxiety Scale, Stimulus-Response Anxiety Inventory, and questions that required participants to report the role of the primary social support provider the participant would seek and the method of communication they would use to communicate with the primary social support provider. Anxiety scores associated with seeking primary social support providers were not significantly different within any situation nor did anxiety scores follow the richness model of anxiety. Regardless of the situation or anxiety, participants frequently sought friends and parents. Participants preferred high richness methods of communication regardless of situation or anxiety, which did not support the richness model of anxiety

Can DMD obtain a scene background in color?

A background model describes a scene without any foreground objects and has a number of applications, ranging from video surveillance to computational photography. Recent studies have introduced the method of Dynamic Mode Decomposition (DMD) for robustly separating video frames into a background model and foreground components. While the method introduced operates by converting color images to grayscale, we in this study propose a technique to obtain the background model in the color domain. The effectiveness of our technique is demonstrated using a publicly available Scene Background Initialisation (SBI) dataset. Our results both qualitatively and quantitatively show that DMD can successfully obtain a colored background model

Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution

In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies that scores will be missing. We address the multimodal fusion problem involving missing modalities (scores) using support vector machines with the Neutral Point Substitution (NPS) method. The approach starts by processing each modality using a kernel. When a modality is missing, at the kernel level, the missing modality is substituted by one that is unbiased with regards to the classification, called a neutral point. Critically, unlike conventional missing-data substitution methods, explicit calculation of neutral points may be omitted by virtue of their implicit incorporation within the SVM training framework. Experiments based on the publicly available Biosecure DS2 multimodal (scores) data set shows that the SVM-NPS approach achieves very good generalization performance compared to the sum rule fusion, especially with severe missing modalities

Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses

Survey questionnaires are often heterogeneous because they contain both quantitative (numeric) and qualitative (text) responses, as well as missing values. While traditional, model-based methods are commonly used by clinicians, we deploy Self Organizing Maps (SOM) as a means to visualise the data. In a survey study aiming at understanding the self-care behaviour of 611 patients with Type-1 Diabetes, we show that SOM can be used to (1) identify co-morbidities; (2) to link self-care factors that are dependent on each other; and (3) to visualise individual patient profiles; In evaluation with clinicians and experts in Type-1 Diabetes, the knowledge and insights extracted using SOM correspond well to clinical expectation. Furthermore, the output of SOM in the form of a U-matrix is found to offer an interesting alternative means of visualising patient profiles instead of a usual tabular form

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Images of the kidneys using dynamic contrast enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers’ kidney DCEMRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of 99% of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD

Cassini in situ observations of long duration magnetic reconnection in Saturn’s magnetotail

Magnetic reconnection is a fundamental process in solar system and astrophysical plasmas, through which stored magnetic energy associated with current sheets is converted into thermal, kinetic and wave energy1, 2, 3, 4. Magnetic reconnection is also thought to be a key process involved in shedding internally produced plasma from the giant magnetospheres at Jupiter and Saturn through topological reconfiguration of the magnetic field5, 6. The region where magnetic fields reconnect is known as the diffusion region and in this letter we report on the first encounter of the Cassini spacecraft with a diffusion region in Saturn’s magnetotail. The data also show evidence of magnetic reconnection over a period of 19?h revealing that reconnection can, in fact, act for prolonged intervals in a rapidly rotating magnetosphere. We show that reconnection can be a significant pathway for internal plasma loss at Saturn6. This counters the view of reconnection as a transient method of internal plasma loss at Saturn5, 7. These results, although directly relating to the magnetosphere of Saturn, have applications in the understanding of other rapidly rotating magnetospheres, including that of Jupiter and other astrophysical bodies