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Using Multivariate Statistics

Data Mining - Concepts and Techniques.

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

https://www.thelancet.com/pdfs/journals/laneur/PIIS1474-4422(13)70124-8.pdf

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

Regularized Seeded Region Growing

Image mosaicing has found a number of applications such as panoramic imaging, digital terrain mapping, ophthalmology, and virtual microscopy. In this paper, we present an automated mosaicing technique for generating mosaics from microscope images. We carry out robust image feature matching and global geometric and radiometric parameter estimation. The images are transformed using the estimated parameters and mosaiced together, producing accurate registration of overlapping regions without visible seams.

Mosaicing of Microscope Images

We describe an accurate new method for determining absolute magnitudes, and hence also K-corrections, that is simpler than most previous methods, being based on a quadratic function of just one suitably chosen observed color. The method relies on the extensive and accurate new set of 129 empirical galaxy template spectral energy distributions from Brown et al. A key advantage of our method is that we can reliably estimate random errors in computed absolute magnitudes due to galaxy diversity, photometric error and redshift error. We derive K-corrections for the five Sloan Digital Sky Survey filters and provide parameter tables for use by the astronomical community. Using the New York Value-Added Galaxy Catalog, we compare our K-corrections with those from kcorrect. Our K-corrections produce absolute magnitudes that are generally in good agreement with kcorrect. Absolute griz magnitudes differ by less than 0.02 mag and those in the u band by ~0.04 mag. The evolution of rest-frame colors as a function of redshift is better behaved using our method, with relatively few galaxies being assigned anomalously red colors and a tight red sequence being observed across the whole 0.0 < z < 0.5 redshift range.

https://iopscience.iop.org/article/10.1088/0004-637X/797/2/104/pdf

An accurate new method of calculating absolute magnitudes and k-corrections applied to the sloan filter set

Patient reported outcome measures (PROMs) can be used by healthcare organisations to inform improvements in service delivery. However, routine collection of PROMs is difficult to achieve across an entire healthcare organisation. An understanding of the use of PROMs within an organisation can provide valuable insights on the purpose, scope and practical considerations of PROMs collection, which can inform implementation of PROMs.We used multiple research methods to assess the use of PROMs in research projects, data registries and clinical care across a healthcare organisation from January 2014 to April 2021. The methods included an audit of ethics applications approved by the organisation's human research ethics committee and registries which the health organisation had contributed data to; a literature review of peer-reviewed journal articles reporting on research projects conducted at the organisation; and a survey of health professionals use of PROMs in research projects, data registries and clinical care. The scope of PROMs was determined by classifying PROMs as either 'specific' to a particular disease and/or condition, or as a 'generic' measure with further classification based on the health domains they measured, using the World Health Organization International Classification Framework. Practical considerations included mode and timing of PROMs administration. Data were described using frequency and proportion.PROMs were used by 22% of research projects (n = 144/666), 68% of data registries (n = 13/19), and 76% of clinical specialties in their clinical care (n = 16/21). Disease specific PROMs were most commonly used: 83% of research projects (n = 130/144), 69% of clinical registries (n = 9/13), and 75% of clinical specialties (n = 12/16). Greater than 80% of research projects, clinical registries and clinical specialties measured health domains relating to both body impairments and participation in daily life activities. The most commonly used generic PROM was the EQ-5D (research projects n = 56/144, 39%; data registries n = 5/13, 38%; clinical specialties n = 4/16, 25%). PROMs used in clinical care were mostly paper-based (n = 47/55, 85%).We have elicited information on the use of PROMs to inform a health organisation wide implementation strategy. Future work will determine clinician and patient acceptability of the EQ-5D, and co-design a system for the collection of PROMs. 

/pdf/a-landscape-assessment-of-the-use-of-patient-reported-2otkfvw5.pdf

A landscape assessment of the use of patient reported outcome measures in research, quality improvement and clinical care across a healthcare organisation

Objective To correlate motor deficit with involvement of corticofugal fibres in patients with subcortical stroke The descending motor corticofugal fibres originate from the primary motor cortex (M1), dorsal and ventral premotor area (PMdv) and supplementary motor area (SMA) Design Retrospective study Setting Single tertiary teaching hospital Participants 57 patients (57% men) with subcortical infarcts on MRI (2009–2011) were included The mean age was 643±144 years Interventions None Primary and secondary outcome measures National Institute of Health Stroke Scale subscores for arm and leg motor deficit at 90 days Results An area under the receiver operating characteristics curve (AUC) for the volume of overlap with infarct (and M1/PMdv/SMA fibres) and motor outcome was calculated The AUC for the association with arm motor deficit from M1 fibres involvement was 080 (95% CI 066 to 094), PMdv was 076 (95% CI 061 to 091) and SMA was 073 (95% CI 058 to 088) The AUC for leg motor deficit from M1 fibres involvement was 069 (95% CI 052 to 085), PMdv was 067 (95% CI 050 to 085), SMA was 066 (95% CI 048 to 084) Conclusions Following subcortical stroke, the correlations between involvement of the corticofugal fibres for upper and lower limbs motor deficit were variable A poor motor outcome was not universal following subcortical stroke

https://bmjopen.bmj.com/content/bmjopen/3/9/e003318.full.pdf

Impact of corticofugal fibre involvement in subcortical stroke

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