Innovation in student engagement in health and social care

Model for student engagement within a learning community at the University of Lincol

Equiangular Tight Frames That Contain Regular Simplices

An equiangular tight frame (ETF) is a type of optimal packing of lines in Euclidean space. A regular simplex is a special type of ETF in which the number of vectors is one more than the dimension of the space they span. In this paper, we consider ETFs that contain a regular simplex, that is, have the property that a subset of its vectors forms a regular simplex. As we explain, such ETFs are characterized as those that achieve equality in a certain well-known bound from the theory of compressed sensing. We then consider the so-called binder of such an ETF, namely the set of all regular simplices that it contains. We provide a new algorithm for computing this binder in terms of products of entries of the ETF\u27s Gram matrix. In certain circumstances, we show this binder can be used to produce a particularly elegant Naimark complement of the corresponding ETF. Other times, an ETF is a disjoint union of regular simplices, and we show this leads to a certain type of optimal packing of subspaces known as an equichordal tight fusion frame. We conclude by considering the extent to which these ideas can be applied to numerous known constructions of ETFs, including harmonic ETFs

A systematic review of the effects of psychosocial interventions on social functioning for middle-aged and older-aged adults with severe mental illness

OBJECTIVES: The number of older adults with severe mental health problems such as schizophrenia is likely to double in the next 20 years. The needs of this patient group change across the life course, but difficulties with social functioning persist into older age. Poorer social functioning is associated with poorer outcomes and has been identified as a priority for intervention by patients themselves. This paper systematically reviews studies examining the effectiveness of psychosocial interventions on social functioning for people with severe mental health problems in later life. METHODS: A systematic review of peer-reviewed journal articles was conducted and databases were searched from inception to December 2017. The review was limited to psychosocial interventions, for mid to older aged adults (≥40 years of age) with severe mental illness that included a validated measure of social functioning. RESULTS: Fifteen studies (17 papers) met inclusion criteria. There was evidence to support skills training interventions that primarily focused on social skills training or integrated mental and physical health interventions. There was not sufficient evidence to recommend any other interventions. CONCLUSIONS: The results highlight the limited nature of interventions designed specifically for older people with severe mental health problems that target social functioning and the need for more robust, large-scale studies in the area. Current evidence suggests that cognitive behaviour therapy can be effective in targeting social functioning in younger age groups, but, as yet, there is insufficient evidence to recommend this intervention for an older population

Equity-Centered Research Methods for Oregon Communities

114 pagesLike many states across the United States, Oregon has a history of using transportation, land use, and housing tools inequitably, which has directed and concentrated benefits to the privileged and harms to underserved communities. Oregon’s past included restrictions on who could own land, redlining and exclusionary zoning, prohibiting more affordable types of housing, and unjust siting of massive highway projects. In recent years, Oregon has begun to acknowledge and take steps to address these inequities. The state’s Land Conservation and Development Commission has updated its Transportation Planning Rules and adopted rules to create and implement the Climate-Friendly and Equitable Communities (CFEC) program. CFEC aims to reduce climate pollution, increase transportation and housing options, and promote equitable land use planning outcomes. The program also requires Oregon’s metropolitan cities and counties to engage in a major equity analysis when conducting a major update of their Transportation System Plans (Oregon Administrative Rule 660-012-0135(3)). Public Administration graduate students researched documentation and materials to develop a methodology that could assist with completing tasks required by sections (a) and (b) of that rule: (a) Assess, document, acknowledge, and address where current and past land use, transportation, and housing policies and effects of climate change have harmed or are likely to harm underserved populations; (b) Assess, document, acknowledge, and address where current and past racism in land use, transportation, and housing has harmed or is likely to harm underserved populations

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes