An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity.

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Do Electronic Medical Records Improve Advance Directive Documentation? A Systematic Review.

BACKGROUND: Documentation rates of advance directives (ADs) remain low. Using electronic medical records (EMRs) could help, but a synthesis of evidence is currently lacking. OBJECTIVES: To evaluate the evidence for using EMRs in documenting ADs and its implications for overcoming challenges associated with their use. DESIGN: Systematic review of articles in English, published from inception of databases to December 2017. DATA SOURCES: PubMed, PsycINFO, EMBASE, and CINAHL. METHODS/MEASUREMENTS: Four databases were searched from inception to December 2017. Randomized and nonrandomized quantitative studies examining the effects of EMRs on creation, storage, or use of ADs were included. All featured an advance care planning process. Evidence was evaluated using the Cochrane Collaboration's risk assessment tool. RESULTS: Fifteen studies were included: 1 randomized controlled trial, 1 randomized pilot, 4 pre-post studies, 4 cross-sectional studies, 1 retrospective cohort study, 1 historical control study, 1 retrospective observational study, 1 retrospective review, and 1 evaluation of an EMR feature. Seven studies showed that EMR-based reminders, AD templates, and decision aids can improve AD documentation rates. Three demonstrated that EMR search functions, decision aids, and automatic identification software can help identify patients who have or need ADs according to certain criteria. Five showed EMRs can create documentation challenges, including locating ADs, and making some patients more likely than others to have an AD. Most studies had an unclear or high risk of bias. CONCLUSIONS: Limited evidence suggests EMRs could be used to help address AD documentation challenges but may also create additional problems. Stronger evidence is needed to more conclusively determine how EMR may assist in population approaches to improving AD documentation.

Adapted K-Core Decomposition and Visualization for Functional Magnetic Resonance Imaging Connectivity Networks.

Medical imaging modalities, such as functional magnetic resonance imaging (fMRI) are being increasingly used to study the human brain. Analysis of the images has led to findings describing diseases, such as schizophrenia and post-traumatic stress disorder. One of the most widely used methods of analysis involves creating functional connectivity network (FCN) abstractions. These summarize the temporal relationships between regions of interest (ROIs) in the brain and can be used to easily compare subjects, e.g. healthy against schizophrenia. Visual analytics is widely used to facilitate such analysis, with existing approaches designed to enable and simplify detailed interpretation of single networks and pairs of networks in comparison. Prior to such detailed analysis, grouping and aggregation is often performed on the data, which is a time consuming and difficult task. Existing methods for doing this are commonly statistical, while others visualize the cohort without presenting vital network details of the individual FCNs. Thus, there is an opportunity for alternative visual analytics to facilitate the grouping by incorporating the network details. Graph decomposition, such as k-core decomposition, can be used to simplify the representation of networks, while retaining these vital network details. In this study, we propose an adapted k-core decomposition algorithm and visualization, which calculates the connected component information of nodes in the FCNs, a key detail in analysis. Our visualization combines this information with the decomposition to display more details about FCNs at a high-level than contemporary approaches. We present a prototype of our method, demonstrating the ability to group and aggregate the data without the loss of vital network details for further detailed analysis.

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging.

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

A systematic review on incentive-driven mobile health technology: As used in diabetes management.

Introduction Mobile health (mHealth) technologies have been shown to improve self-management of chronic diseases, such as diabetes. However, mHealth tools, e.g. apps, often have low rates of retention, eroding their potential benefits. Using incentives is a common mechanism for engaging, empowering and retaining patients that is applied by mHealth tools. We conducted a systematic review aiming to categorize the different types of incentive mechanisms employed in mHealth tools for diabetes management, which we defined as incentive-driven technologies (IDTs). As an auxiliary aim, we also analyzed barriers to adoption of IDTs. Methods Literature published in English between January 2008-August 2014 was identified through searching leading publishers and indexing databases: IEEE, Springer, Science Direct, NCBI, ACM, Wiley and Google Scholar. Results A total of 42 articles were selected. Of these, 34 presented mHealth tools with IDT mechanisms; Education was the most common mechanism ( n = 21), followed by Reminder ( n = 11), Feedback ( n = 10), Social ( n = 8), Alert ( n = 5), Gamification ( n = 3), and Financial ( n = 2). Many of these contained more than one IDT ( n = 19). The remaining eight articles, from which we defined barriers for adoption, were review papers and a qualitative study of focus groups and interviews. Discussion While mHealth technologies have advanced over the last five years, the core IDT mechanisms have remained consistent. Instead, IDT mechanisms have evolved with the advances in technology, such as moving from manual to automatic content delivery and personalization of content. Conclusion We defined the concept of IDT to be core features designed to act as motivating mechanisms for retaining and empowering users. We then identified seven core IDT mechanisms that are used by mHealth tools for diabetes management and classified 34 articles into these categories.

Medical futility.

We examine the concept of medical futility by addressing several questions. Should doctors be attempting treatments that have little likelihood of achieving the goals of medicine? What are the goals of medicine? Can we agree when medical treatment fails to achieve such goals? What should the physician do and not do under such circumstances? Exploring these issues has forced us to revisit the doctor-patient relationship and the relationship of the medical profession to society. Medical futility has both quantitative and qualitative components. We argue that medical futility is the unacceptable likelihood of achieving an effect that the patient has the capacity to appreciate as a benefit. Medicine today has the capacity to achieve a multitude of effects, but none creates a benefit unless the patient has the capacity to appreciate it. In the futility debate wherein some critics have failed or refused to define medical futility, an important area of medicine has been neglected - palliative care - the physician's obligation to alleviate suffering, enhance wellbeing, and support the dignity of the patient at the end of life. To provide a broader perspective we end with a view from Germany.

Data processing and presentation for a personalised, image-driven medical graphical avatar.

With the continuing digital revolution in the healthcare industry, patients are being confronted with the difficult task of managing their digital medical data. Current personal health record (PHR) systems are able to store and consolidate this data, but they are limited in providing tools to facilitate patients' understanding and management of the data. One reason for this stems from the limited use of contextual information, especially in presenting spatial details such as in volumetric images and videos, as well as time-based temporal data. Further, lack of meaningful visualisation techniques exist to represent the data stored in PHRs. In this paper we propose a medical graphical avatar (MGA) constructed from whole-body patient images, and a navigable timeline of the patient's medical records. A data mapping framework is presented that extracts information from medical multimedia data such as images, video and text, to populate our PHR timeline, while also embedding spatial and textual annotations such as regions of interest (ROIs) that are automatically derived from image processing algorithms. We developed a prototype to process the various forms of PHR data and present the data in a graphical avatar. We analysed the usefulness of our system under various scenarios of patient data use and present preliminary results that indicate that our system performs well on standard consumer hardware.

[The physician between guidelines and patient individuality]. / Der Arzt zwischen Leitlinien und Patientenindividualität.

Guidelines and individual patient care are not contradictory but rather complementary. They provide orientation to physicians in situations of complexity and lack of clarity. In addition, they simplify and speed up the process of practical implementation of new medical knowledge in the patient care. Guidelines are also a systematic principle that helps to recognize and avoid diagnostic and therapeutic uncertainty, as well as to protect patients. Furthermore, well-understood guidelines mean that the physician is less burdened and can dedicate himself to something currently missing in the physician-patient relationship: time for comprehensive medical interviews.