4,521 research outputs found
The role of nursing in multimorbidity care
Background
Multimorbidity (the co-occurrence of two or more chronic conditions in the same person) affects around one in three persons, and it is strongly associated with a range of negative outcomes including worsening physical function, increased health care use, and premature death. Due to the way healthcare is provided to people with multimorbidity, treatment can become burdensome, fragmented and inefficient. In people with palliative conditions, multimorbidity is increasingly common. Better models of care are needed.
Methods
A mixed-methods programme of research designed to inform the development of a nurse-led intervention for people with multimorbidity and palliative conditions. A mixed-methods systematic review explored nurse-led interventions for multimorbidity and their effects on outcomes. A cross-sectional study of 63,328 emergency department attenders explored the association between multimorbidity, complex multimorbidity (≥3 conditions affecting ≥3 body systems), and disease-burden on healthcare use and inpatient mortality. A focussed ethnographic study of people with multimorbidity and life-limiting conditions and their carers (n=12) explored the concept of treatment burden.
Findings
Nurse-led interventions for people with multimorbidity generally focus on care coordination (i.e., case management or transitional care); patients view them positively, but they do not reliably reduce health care use or costs. Multimorbidity and complex multimorbidity were significantly associated with admission from the emergency department and reattendance within 30 and 90 days. The association was greater in those with more conditions. There was no association with inpatient mortality. People with multimorbidity and palliative conditions experienced treatment burden in a manner consistent with existing theoretical models. This thesis also noted the effect of uncertainty on the balance between capacity and workload and proposes a model of how these concepts relate to one another.
Discussion
This thesis addresses a gap in what is known about the role of nurses in providing care to the growing number of people with multimorbidity. A theory-based nurse-led intervention is proposed which prioritises managing treatment burden and uncertainty.
Conclusions
Nursing in an age of multimorbidity necessitates a perspective shift which conceptualises chronic conditions as multiple overlapping phenomena situated within an individual. The role of the nurse should be to help patients navigate the complexity of living with multiple chronic conditions
Creating granular climate zones for future-proof building design in the UK
This is the final version. Available on open access from Elsevier via the DOI in this recordData availability:
Datasets related to this article can be found at https://catalogue.ceda.ac.uk, hosted at the CEDA archive.Climate zones play an important role in promoting climate responsive building design and implementing climate-specific prescriptions in national building standards and regulations. The existing studies on climate zoning are subject to several limitations, i.e. the incapability of distinguishing microclimates and the lack of consideration of climate change. In this research, we propose a two-tiered ensemble clustering method for the identification of granular climate zones using the projections of future climate. The first tier identifies primary climate zones using a combination of climatic features and geographical locations, whereas the second tier identifies distinct local variations within each primary climate zone using the temperature related features. The proposed ensemble clustering model is applied to the UK to create a mapping of granular climate zones for future proofing building design. The method identified 14 distinct primary zones and distinguished microclimates at a range of scales from large urban areas, such as the Greater London Area, to national parks, such as Dartmoor and the Pennines. The identified mapping resolves two major obstacles in the creation and usage of weather data for building performance assessment in the UK, i.e. the lack of guidance for selecting weather files, and the absence of scientific rationale for representing the UK climate using the current 14 locations.Innovate U
Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study
Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations
Applications of Deep Learning Models in Financial Forecasting
In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting.
The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with
approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data.
The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to
financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided
Advances in machine learning algorithms for financial risk management
In this thesis, three novel machine learning techniques are introduced to address distinct
yet interrelated challenges involved in financial risk management tasks. These approaches
collectively offer a comprehensive strategy, beginning with the precise classification of credit
risks, advancing through the nuanced forecasting of financial asset volatility, and ending
with the strategic optimisation of financial asset portfolios.
Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk
assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture
modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed
using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression
model is then applied to predict the probability of default using the heuristically balanced
datasets. The results underscore the effectiveness of our proposed technique, with superior
performance observed in comparison to other imbalanced preprocessing approaches. This
advancement in credit risk classification lays a solid foundation for understanding individual
financial behaviours, a crucial first step in the broader context of financial risk management.
Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a
Triple Discriminator Generative Adversarial Network with a continuous wavelet transform
is proposed. The proposed model has the ability to decompose volatility time series into
signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform
component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a
Generative Adversarial Network consisting of triple Discriminator and Generator networks.
The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised
loss and reconstruction loss as part of its framework. Data from nine financial assets are
employed to demonstrate the effectiveness of the proposed model. This approach not only
enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis.
Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio
optimisation using historical Low, High, and Close prices of assets as input with weights of
assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return
on investment based on deep reinforcement learning. To provide more learning stability in
an online training process, a Markov Differential Sharpe Ratio reward function has been
proposed as the reinforcement learning objective function. Additionally, a Multi-Memory
Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout
a specified trading period. The use of the insights gained from volatility forecasting into
this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving
superior results based on risk-adjusted reward performance measures.
In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the
accuracy of credit risk classification, through the improvement and understanding of market
volatility, to optimisation of investment strategies. These methodologies collectively show
the potential of the use of machine learning to improve financial risk management
PANCREATODUODENECTOMY FOR MALIGNANCY: FACTORS INFLUENCING SURGICAL AND ONCOLOGICAL OUTCOMES
Introduction:
Fit patients with a resectable pancreatic head adenocarcinoma (PDAC), ampullary adenocarcinoma (AA) or distal cholangiocarcinoma (CC) may be offered pancreatoduodenectomy (PD) with curative-intent. However, perioperative morbidity and cancer recurrence rates are high. This thesis aimed to explore the factors influencing PD outcomes. A focus was placed on nutrition, postoperative complications, and recurrence in AA patients. It is hoped the findings will guide patient selection/consenting and have implications for patient management.
Methods:
A retrospective cohort study of patients who underwent PD for histologically-confirmed malignancy was carried out (2012-2015). Twenty-nine centres from eight countries were involved. Data on the following were collected: preoperative comorbidities and investigations, neoadjuvant treatment, operative details, postoperative complications, histology, adjuvant treatment, cancer recurrence, palliative treatment, and overall survival (OS).
Results:
In total, 1484 patients were included; 885 (59.6%), 394 (26.5%) and 205 (13.8%) had PDAC, AA and CC, respectively. Overall morbidity, major morbidity (Clavien-Dindo grade 11 ≥III) and 90-day mortality rates were 53.4%, 16.9% and 3.8%, respectively. A high body mass index (BMI), an American Society of Anesthesiologists (ASA) grade >II and a classic Whipple approach all correlated with morbidity. Additionally, ASA grade >II patients were at increased risk of major morbidity and a raised BMI correlated with a greater risk of pancreatic leak. Almost half of the cohort received nutritional support (NS). Of these, 55.6% received parenteral nutrition (PN). In total, 19.6% of the patients who had an uneventful postoperative recovery received PN. Among the PDAC cohort, commencing adjuvant chemotherapy (AC) correlated with improved OS, and those who experienced major morbidity commenced AC less frequently. Among the AA cohort, 176 patients (44.7%) developed recurrence and the median time-to-recurrence was 14 months. Local only, local and distant, and distant only recurrence affected 34, 41 and 94 patients, respectively (site unknown: 7). A higher number of resected nodes, histological T stage >II, lymphatic invasion, perineural invasion (PNI), peripancreatic fat invasion (PPFI) and ≥1 positive resection margin all correlated with AA recurrence. Further, ≥1 positive margin, PPFI and PNI were associated with reduced time-to-recurrence.
Conclusions:
A considerable number of the patients that had an uneventful recovery received PN. Patients with a high BMI or ASA grade had worse perioperative outcomes. Those who experienced major morbidity commenced AC less frequently. Numerous histopathological predictors of AA recurrence and reduced time-to-recurrence were identified
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