8,589 research outputs found
East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series
Academic geriatric medicine in Leicester
.
There has never been a better time to consider joining us. We have recently appointed a
Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton,
who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic
opportunities to support students in their academic pursuits through a well-established
intercalated BSc programme, and routes on through such as ACF posts, and a successful
track-record in delivering higher degrees leading to ACL post. We collaborate strongly
with Health Sciences, including academic primary care. See below for more detail on our
existing academic set-up.
Leicester Academy for the Study of Ageing
We are also collaborating on a grander scale, through a joint academic venture focusing
on ageing, the âLeicester Academy for the Study of Ageingâ (LASA), which involves the
local health service providers (acute and community), De Montfort University; University
of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK.
Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been
joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen
Harrison Dening has also recently been appointed an Honorary Chair.
LASA aims to improve outcomes for older people and those that care for them that takes
a person-centred, whole system perspective. Our research will take a global perspective,
but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland,
including building capacity. We are undertaking applied, translational, interdisciplinary
research, focused on older people, which will deliver research outcomes that address
domains from: physical/medical; functional ability, cognitive/psychological; social or
environmental factors. LASA also seeks to support commissioners and providers alike for
advice on how to improve care for older people, whether by research, education or
service delivery. Examples of recent research projects include: âLocal History CafĂ©â
project specifically undertaking an evaluation on loneliness and social isolation; âBetter
Visitsâ project focused on improving visiting for family members of people with dementia
resident in care homes; and a study on health issues for older LGBT people in Leicester.
Clinical Geriatric Medicine in Leicester
We have developed a service which recognises the complexity of managing frail older
people at the interface (acute care, emergency care and links with community services).
There are presently 17 consultant geriatricians supported by existing multidisciplinary
teams, including the largest complement of Advance Nurse Practitioners in the country.
Together we deliver Comprehensive Geriatric Assessment to frail older people with
urgent care needs in acute and community settings.
The acute and emergency frailty units â Leicester Royal Infirmary
This development aims at delivering Comprehensive Geriatric Assessment to frail older
people in the acute setting. Patients are screened for frailty in the Emergency
Department and then undergo a multidisciplinary assessment including a consultant
geriatrician, before being triaged to the most appropriate setting. This might include
admission to in-patient care in the acute or community setting, intermediate care
(residential or home based), or occasionally other specialist care (e.g. cardiorespiratory).
Our new emergency department is the countyâs first frail friendly build and includes
fantastic facilities aimed at promoting early recovering and reducing the risk of hospital
associated harms.
There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we
have been examining geriatric outreach to oncology and surgery as part of an NIHR
funded study.
We are home to the Acute Frailty Network, and those interested in service developments
at the national scale would be welcome to get involved.
Orthogeriatrics
There are now dedicated hip fracture wards and joint care with anaesthetists,
orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone
disease that run clinics.
Community work
Community work will consist of reviewing patients in clinic who have been triaged to
return to the community setting following an acute assessment described above.
Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will
work closely with local GPs with support from consultants to deliver post-acute, subacute,
intermediate and rehabilitation care services.
Stroke Medicine
24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK
and along with the high standard of vascular surgery locally means one of the best
performances regarding carotid intervention
New Statistical Transfer Learning Models for Health Care Applications
abstract: Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinsonâs Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patientâs knowledge for building a better predictive model. When modeling a target patient, not every patientâs information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of âtransferrableâ patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
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Beyond Protecting Genetic Privacy: Understanding Genetic Discrimination Through its Disparate Impact on Racial Minorities
At the very end of the last century, scientists produced the first draft of the whole human genetic sequence. But that was just the first step; the hard work of the first few decades of this century will be to learn more about how to apply genetic information to improve health. As the pace of technological development accelerates and we learn more about what genetic variations mean about individual human characteristics and health risks, so too does the risk and consequences of the misuse of such information become more significant. The principal answer to this challenge has been to safeguard privacy by constructing legal and technical barriers that conceal and anonymize genetic information. While it may be a worthwhile objective, ultimately privacy protections will likely fail in practice. If this is so, how can we prevent genetic information from being used to categorize, stigmatize, and subordinate? This Note approaches this problem by analyzing the African American experience with genetic discrimination in the United States. African Americans have confronted the adverse consequences of genetic research in ways that can serve as a foundation to understand future threats posed to racial minorities and everyone in society, as genetic testing increases in prevalence and the privacy of genetic information is unable to be protected. Studying the real history of genetic discrimination, rather than merely speculating about what may happen, can point toward policy solutions that go beyond âgenetic privacy.â As genetic information becomes more plentiful and valuable, policies to prevent the misuse of that information will benefit everyone, regardless of race or ethnicity
2023 IMSAloquium
Welcome to IMSAloquium 2023. This is IMSAâs 36 th year of leading in educationalinnovation, and the 35th year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1033/thumbnail.jp
Chronic disease prevention in college students: assessment of perception and intention to use a health management app
2018 Summer.Includes bibliographical references.The relationship between intention to use a hypothetical health management app and other variables from a conceptual framework of the Health Belief Model (HBM) and the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) was assessed using a convenience sample of college students (N= 176). The self-reported online survey measured perceived susceptibility to chronic diseases, perceived seriousness of chronic diseases, perceived benefits of the app, perceived barriers to the app, cues to action, social influence, facilitating conditions and intention to use the app, on 5-point Likert type scales adapted from previous studies. Multiple linear regression was used to determine relationships between the predictor variables and criterion variable. The results of the data analysis showed that individually there were a low perception of susceptibility to diseases, perception of barriers to the app and perception of social influence, and a high perception of seriousness of diseases, perception of benefits of the app, cues to action, facilitating conditions, and intention to use the app. Perceived susceptibility, perceived benefits, perceived barriers, social influence and facilitating conditions had a significant influence on college students' intention to use the app to manage different aspects of their health. However, perceived seriousness and cues to action were not found to predict college students' intention to use the health management app
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Mundane is the New Radical: The Resurgence of Energy Megaprojects and Implications for the Global South [Opinion]
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