The Impact of a Nurse Engagement Model Implementation on Patient Outcomes

As key figures in health care today, nurses contribute greatly to the provision of safe, quality care delivery in the acute care setting. Increasing evidence indicates that patient outcomes are better in hospitals with good nurse work environments, including those with a professional practice model of care delivery. With health care organizations currently facing demanding competitive markets, multiple governmental regulations, various accreditations, financial stability, patient safety concerns, patient and family satisfaction, sustainable quality metrics, resource stewardship, and workforce issues such as turnover and shortages, it is imperative that organizations look to assuring they have a strong nursing workforce. Professional practice models give meaning to the care nurses deliver through nursing theory and guide nursing practice. Several studies suggest that increasing engagement can improve patient and nurse outcomes, thereby suggesting that professional practice model implementation may be a method to consider. The purpose of this project was to evaluate the effectiveness of a uniquely designed employee engagement implementation model for nursing and to explore its impact on patient outcomes and nurse engagement

Segmentation and Unsupervised Adversarial Domain Adaptation Between Medical Imaging Modalities

Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives. Furthermore, creating methods that generically, accurately and successfully process unlabelled data would be a major breakthrough in deep learning. This thesis aims to address both these challenges by exploring and improving current methods involving adversarial discriminative domain adaptation (ADDA) on multimodal imaging, and address weaknesses, not only in ADDA, but also in the general adversarial discriminative cases. More specifically, this thesis - applies convolutional neural networks to segment soft tissue sarcomas in PET, CT and MRI modalities, and to the author's best knowledge achieves state-of-the-art results, - explores unsupervised adversarial discriminative domain adaptation on segmentation of soft tissue sarcoma tumors between permutations of PET, CT and MRI and - demonstrates weaknesses in state-of-the-art adversarial discriminative training, and finally - improves and provides groundwork for further research on said techniques. Additionally, the thesis will also provide strong fundamental background for applying ADDA for use in medical modalities, including a solid introduction to deep learning in medical imaging, both from a theoretical and practical aspect

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data. In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, specifically designed to handle missing data. We focus on the problem of detecting surgical site infection in patients by analyzing time series of their blood sample measurements and we compare the results obtained with different RNN-based classifiers

Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series

The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths. If these challenges are not properly handled, the resulting clusters might be of suboptimal quality. As a key solution, we present a joint clustering and feature learning framework for time series based on deep learning. For a given set of time series, we train a recurrent network to represent, or embed, each time series in a vector space such that a divergence-based clustering loss function can discover the underlying cluster structure in an end-to-end manner. Unlike previous approaches, our model inherently handles multivariate time series of variable lengths and does not require specification of a distance-measure in the input space. On a diverse set of benchmark datasets we illustrate that our proposed Recurrent Deep Divergence-based Clustering approach outperforms, or performs comparable to, previous approaches

Engineering Virtuous health habits using Emotion and Neurocognition: Flexibility for Lifestyle Optimization and Weight management (EVEN FLOW)

Interventions to preserve functional independence in older adults are critically needed to optimize ‘successful aging’ among the large and increasing population of older adults in the United States. For most aging adults, the management of chronic diseases is the most common and impactful risk factor for loss of functional independence. Chronic disease management inherently involves the learning and adaptation of new behaviors, such as adopting or modifying physical activity habits and managing weight. Despite the importance of chronic disease management in older adults, vanishingly few individuals optimally manage their health behavior in the service of chronic disease stabilization to preserve functional independence. Contemporary conceptual models of chronic disease management and health habit theory suggest that this lack of optimal management may result from an underappreciated distinction within the health behavior literature: the behavioral domains critical for initiation of new behaviors (Initiation Phase) are largely distinct from those that facilitate their maintenance (Maintenance Phase). Psychological factors, particularly experiential acceptance and trait levels of openness are critical to engagement with new health behaviors, willingness to make difficult lifestyle changes, and the ability to tolerate aversive affective responses in the process. Cognitive factors, particularly executive function, are critical to learning new skills, using them effectively across different areas of life and contextual demands, and updating of skills to facilitate behavioral maintenance. Emerging data therefore suggests that individuals with greater executive function are better able to sustain behavior changes, which in turn protects against cognitive decline. In addition, social and structural supports of behavior change serve a critical buffering role across phases of behavior change. The present review attempts to address these gaps by proposing a novel biobehavioral intervention framework that incorporates both individual-level and social support system-level variables for the purpose of treatment tailoring. Our intervention framework triangulates on the central importance of self-regulatory functioning, proposing that both cognitive and psychological mechanisms ultimately influence an individuals’ ability to engage in different aspects of self-management (individual level) in the service of maintaining independence. Importantly, the proposed linkages of cognitive and affective functioning align with emerging individual difference frameworks, suggesting that lower levels of cognitive and/or psychological flexibility represent an intermediate phenotype of risk. Individuals exhibiting self-regulatory lapses either due to the inability to regulate their emotional responses or due to the presence of executive functioning impairments are therefore the most likely to require assistance to preserve functional independence. In addition, these vulnerabilities will be more easily observable for individuals requiring greater complexity of self-management behavioral demands (e.g. complexity of medication regimen) and/or with lesser social support. Our proposed framework also intuits several distinct intervention pathways based on the profile of self-regulatory behaviors: we propose that individuals with intact affect regulation and impaired executive function will preferentially respond to ‘top-down’ training approaches (e.g., strategy and process work). Individuals with intact executive function and impaired affect regulation will respond to ‘bottom-up’ approaches (e.g., graded exposure). And individuals with impairments in both may require treatments targeting caregiving or structural supports, particularly in the context of elevated behavioral demands

Individual differences in regulatory focus predict neural response to reward

Although goal pursuit is related to both functioning of the brain's reward circuits and psychological factors, the literatures surrounding these concepts have often been separate. Here, we use the psychological construct of regulatory focus to investigate individual differences in neural response to reward. Regulatory focus theory proposes two motivational orientations for personal goal pursuit: (1) promotion, associated with sensitivity to potential gain, and (2) prevention, associated with sensitivity to potential loss. The monetary incentive delay task was used to manipulate reward circuit function, along with instructional framing corresponding to promotion and prevention in a within-subject design. We observed that the more promotion oriented an individual was, the lower their ventral striatum response to gain cues. Follow-up analyses revealed that greater promotion orientation was associated with decreased ventral striatum response even to no-value cues, suggesting that promotion orientation may be associated with relatively hypoactive reward system function. The findings are also likely to represent an interaction between the cognitive and motivational characteristics of the promotion system with the task demands. Prevention orientation did not correlate with ventral striatum response to gain cues, supporting the discriminant validity of regulatory focus theory. The results highlight a dynamic association between individual differences in self-regulation and reward system function

On the propagation of electromagnetic radiation in the field of a plane gravitational wave

The propagation of free electromagnetic radiation in the field of a plane gravitational wave is investigated. A solution is found one order of approximation beyond the limit of geometrical optics in both transverse--traceless (TT) gauge and Fermi Normal Coordinate (FNC) system. The results are applied to the study of polarization perturbations. Two experimental schemes are investigated in order to verify the possibility to observe these perturbations, but it is found that the effects are exceedingly small.Comment: 13 pages; revtex; accepted for publication in Class. Quantum Gra

Evaluation of Life Events in Major Depression: Assessing Negative Emotional Bias

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137243/1/cpp2033.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137243/2/cpp2033_am.pd