5,249 research outputs found

    Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

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    This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

    The Impact of Ethnicity on The Trajectory of Depression Symptom Change During Psychological Interventions

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    Uncovering variations in depression symptom change across ethnic groups during psychological intervention could improve understanding of differences in treatment response. This study aimed to: (1) identify trajectories of change in treatment; (2) ascertain if depression symptom trajectories varied between BAME and White populations; (3) investigate if sociodemographic and treatment variables predicted association with the identified trajectories; and (4) examine if ethnic groups predicted different trajectory memberships. Adults (N = 17109) with depression and recorded ethnicity were included in the analysis. Depressive symptoms were measured using the Patient Health Questionnaire-9, and co-occurring anxiety was measured using the Generalised Anxiety Disorder-7. Growth Mixture Modelling (GMM) was employed to identify trajectories of symptom change, and multinomial logistic regressions were used to identify ethnicity and other pre-treatment variables associated with trajectory membership. GMM resulted in three depression trajectories of change and four anxiety trajectories. There was a high proportion of patients who did not respond to treatment. Pre-treatment variables that predicted Non-response were: ethnic minority, unemployment, deprived areas, prescribed medications, higher baseline anxiety and depression scores, and long-term physical health conditions. Asian patients had higher odds than White patients associated with trajectories that had high severity for both outcome measures. Black, Other, Mixed-heritage, and Chinese populations were no different from White populations in depressive treatment responses after adjusting for an index of multiple deprivations (IMD). Results have implications for identifying patients at risk of non-response such that clinicians can tailor culturally sensitive interventions for ethnic minority patients

    A study on effects of safety checklists emphasizing quality of complication data

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    Introduction: Despite increased focus on patient safety, complication rates in hospitals have remained unchanged with reports ranging between one out of twenty patients and one out of four patients, often related to surgery. However, half of the complications may be prevented throughout the surgical pathway. To inform and study effects of targeted patient safety interventions requires patient outcome data of high accuracy. Introduction of the World Health Organization surgical safety checklists (WHO SSC) has been reported to increase safety, also in our hospital. Aims: The overall objective for the study was to investigate effects of using safety checklists on patient outcomes in medicine. Further, to evaluate effects of adding a validated Norwegian version of the pre- and postoperative parts of the SURPASS checklists in combination with the established WHO SSC on emergency reoperations, 30-day unplanned readmissions, 30-day mortality and length of hospital stay, in addition to verified in-hospital complications using a reliable and validated method. Methods: In the first study, we conducted a systematic literature search in Cochrane Library, MEDLINE, EMBASE and Web of Science on effects on patient outcomes of using safety checklists in medicine. Following the PRISMA guidelines ensured transparency of reporting. The studies were eligible if they quantitatively reported possible effects of using safety checklists. In the second study, validation of a Norwegian version of the pre- and postoperative SURPASS checklists in combination with the established WHO SSC was performed in one neurosurgical department. Adaptation and validation of the new checklists were in accordance to guidelines from the WHO included forth- and back translation, testing the content in clinical practice, focus groups, expert panels, and final approval of the checklists. The third study used a prospective observational design to investigate complications in surgical admissions using two different methods. Utilising the Global Trigger Tool (GTT) and the International Classification of Diseases 10th version (ICD-10) identified and verified in-hospital complications in the same admissions with GTT appointed as the reference standard. Tests were performed to investigate strength of method agreement of estimating complications. In the fourth study, the validated pre- and postoperative SURPASS checklists were implemented as an add-on to the established WHO SSC using a Stepped Wedge Cluster Controlled Trial (SWCCT) design in three surgical clusters, each serving as their own controls (neurosurgery, orthopaedics and gynaecology) in one hospital. One separate department in the intervention hospital and two external hospitals without new checklists constituted parallel controls. Effects on verified in-hospital complications, emergency reoperations, 30-day readmissions, 30-day mortality and length of hospital stay were investigated over 29 months from November 2012 through March 2015. Results: Thirty-four studies met the inclusion criteria of the systematic review of the literature showing improvements in four groups of patient outcomes: morbidity and mortality; adherence to guidelines; human factors; and adverse events. None of the included studies reported on checklist use resulting in decreased patient safety (Study I). Translation of the pre- and postoperative SURPASS checklists in combination with the WHO SSC was completed and reached face validity. Testing of the content was performed for 29 neurosurgical procedures with all checklist users (ward nurse and physicians, surgeons, anaesthesiologists, operating theatre nurses, post-anaesthetic care unit nurses, and discharging physicians and nurses). Focus groups revealed that wording needed to be adapted to clinical practice and that checklist items challenged existing workflow. The expert panels scored content validity to > 80 %. All the steps involved adjustments to the checklist content. The final back translated SURPASS checklist version was approved by the Dutch copyright holder (Study II). In 700 random surgical admissions complications were identified in 30.3 % (298/700) using the GTT method. Extracted ICD-10 codes indicating a complication yielded a rate of 47.4 % (332/700) in the same admissions. However, when excluding ICD-10 codes representing conditions present on admission, in-hospital complications were verified for 20.1 % (141/700) of the admissions. After the verification procedure, agreement of complications between findings using both methods increased from 68.3 % to 83.3 % (Study III). The fourth study compared 3,892 before and 5,117 procedures after the pre- and postoperative SURPASS checklists implementation in intervention clusters. In addition, investigations of 9,678 surgical procedures in parallel control hospitals were performed. Crude analysis of in-hospital complications showed an increase of complications from 14.7 % to 16.5 % (p=0.025). However, in-hospital complications decreased in adjusted intention to treat analyses (Odds Ratio (OR): 0.73; 95% Confidence Interval (CI): 0.54 to 0.98; p = 0.035). Logistic regression on effects of the SURPASS checklists, show a significant decrease in in-hospital complications (OR: 0.70; 95% CI: 0.50 to 0.98; p = 0.036) and emergency reoperations (OR: 0.42; 95% CI: 0.23 to 0.76; p = 0.004) with full compliance to the preoperative SURPASS checklist in adjusted analysis. With obtained full compliance to the postoperative SURPASS checklists 30-day readmissions were decreased (OR: 0.32; 95% CI: 0.16 to 0.64; p = 0.001) in adjusted analysis. Thirty-day mortality and length of hospital stay remained unchanged. For parallel control hospitals, the in-hospital complications increased, whereas emergency reoperations, 30-day readmissions and 30-day mortality were unchanged. Conclusions The systematic review of the literature concluded that use of safety checklists may have positive impact on patient outcomes as more clinicians adhere to standardised guidelines and procedures; improve human factors; and reduce adverse events, morbidity and mortality. We need more studies with strong study designs investigating effects of checklists used throughout the surgical pathway. The first Norwegian version of the pre- and postoperative SURPASS checklists in combination with the already established WHO SSC was validated following guidelines on translation and adaptation from the WHO. Using ICD-10 codes to monitor complications increased accuracy significantly when codes indicating complications were verified to have emerged in-hospital. Full compliance with the pre- and postoperative SURPASS checklists were associated with reduced in-hospital complications, emergency reoperations and 30-day readmissions when added to the already established intraoperative WHO SSC

    Transactional Data Structures

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    Continual learning from stationary and non-stationary data

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    Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims

    Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

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    Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

    Understanding and Optimizing Flash-based Key-value Systems in Data Centers

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    Flash-based key-value systems are widely deployed in today’s data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations. In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs
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