Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

Data Mining Practical Machine Learning Tools and Techniques

In this article the author reviews research evidence on the prevalence of mental disorders in lesbians, gay men, and bisexuals (LGBs) and shows, using meta-analyses, that LGBs have a higher prevalence of mental disorders than heterosexuals. The author offers a conceptual framework for understanding this excess in prevalence of disorder in terms of minority stress— explaining that stigma, prejudice, and discrimination create a hostile and stressful social environment that causes mental health problems. The model describes stress processes, including the experience of prejudice events, expectations of rejection, hiding and concealing, internalized homophobia, and ameliorative coping processes. This conceptual framework is the basis for the review of research evidence, suggestions for future research directions, and exploration of public policy implications. The study of mental health of lesbian, gay, and bisexual (LGB) populations has been complicated by the debate on the classification of homosexuality as a mental disorder during the 1960s and early 1970s. That debate posited a gay-affirmative perspective, which sought to declassify homosexuality, against a conservative perspective, which sought to retain the classification of homosexuality as a mental disorder (Bayer, 1981). Although the debate on classification ended in 1973 with the removal of homosexuality from the second edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association, 1973), its heritage has lasted. This heritage has tainted discussion on mental health of lesbians and gay men by associating— even equating— claims that LGB people have higher prevalences of mental disorders than heterosexual people with the historical antigay stance and the stigmatization of LGB persons (Bailey, 1999). However, a fresh look at the issues should make it clear that whether LGB populations have higher prevalences of mental disorders is unrelated to the classification of homosexuality as a mental disorder. A retrospective analysis would suggest that the attempt to find a scientific answer in that debate rested on flawed logic. The debated scientific question was, Is homosexuality a mental disorder? The operationalized research question that pervaded the debate was, Do homosexuals have high prevalences of mental disorders? But the research did not accurately operationalize the scientific question. The question of whether homosexuality should be considered a mental disorder is a question about classification. It can be answered by debating which behaviors, cognitions, or emotions should be considered indicators of a mental

/pdf/prejudice-social-stress-and-mental-health-in-lesbian-gay-and-2eymy1qdt5.pdf

Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence

ABI
: ankle–brachial index
ACCORD
: Action to Control Cardiovascular Risk in Diabetes
ADVANCE
: Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation
AGREE
: Appraisal of Guidelines Research and Evaluation
AHA
: American Heart Association
apoA1
: apolipoprotein A1
apoB
: apolipoprotein B
CABG
: coronary artery bypass graft surgery
CARDS
: Collaborative AtoRvastatin Diabetes Study
CCNAP
: Council on Cardiovascular Nursing and Allied Professions
CHARISMA
: Clopidogrel for High Athero-thrombotic Risk and Ischemic Stabilisation, Management, and Avoidance
CHD
: coronary heart disease
CKD
: chronic kidney disease
COMMIT
: Clopidogrel and Metoprolol in Myocardial Infarction Trial
CRP
: C-reactive protein
CURE
: Clopidogrel in Unstable Angina to Prevent Recurrent Events
CVD
: cardiovascular disease
DALYs
: disability-adjusted life years
DBP
: diastolic blood pressure
DCCT
: Diabetes Control and Complications Trial
ED
: erectile dysfunction
eGFR
: estimated glomerular filtration rate
EHN
: European Heart Network
EPIC
: European Prospective Investigation into Cancer and Nutrition
EUROASPIRE
: European Action on Secondary and Primary Prevention through Intervention to Reduce Events
GFR
: glomerular filtration rate
GOSPEL
: Global Secondary Prevention Strategies to Limit Event Recurrence After MI
GRADE
: Grading of Recommendations Assessment, Development and Evaluation
HbA1c
: glycated haemoglobin
HDL
: high-density lipoprotein
HF-ACTION
: Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing
HOT
: Hypertension Optimal Treatment Study
HPS
: Heart Protection Study
HR
: hazard ratio
hsCRP
: high-sensitivity C-reactive protein
HYVET
: Hypertension in the Very Elderly Trial
ICD
: International Classification of Diseases
IMT
: intima-media thickness
INVEST
: International Verapamil SR/Trandolapril
JTF
: Joint Task Force
LDL
: low-density lipoprotein
Lp(a)
: lipoprotein(a)
LpPLA2
: lipoprotein-associated phospholipase 2
LVH
: left ventricular hypertrophy
MATCH
: Management of Atherothrombosis with Clopidogrel in High-risk Patients with Recent Transient Ischaemic Attack or Ischaemic Stroke
MDRD
: Modification of Diet in Renal Disease
MET
: metabolic equivalent
MONICA
: Multinational MONItoring of trends and determinants in CArdiovascular disease
NICE
: National Institute of Health and Clinical Excellence
NRT
: nicotine replacement therapy
NSTEMI
: non-ST elevation myocardial infarction
ONTARGET
: Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial
OSA
: obstructive sleep apnoea
PAD
: peripheral artery disease
PCI
: percutaneous coronary intervention
PROactive
: Prospective Pioglitazone Clinical Trial in Macrovascular Events
PWV
: pulse wave velocity
QOF
: Quality and Outcomes Framework
RCT
: randomized clinical trial
RR
: relative risk
SBP
: systolic blood pressure
SCORE
: Systematic Coronary Risk Evaluation Project
SEARCH
: Study of the Effectiveness of Additional Reductions in Cholesterol and
SHEP
: Systolic Hypertension in the Elderly Program
STEMI
: ST-elevation myocardial infarction
SU.FOL.OM3
: SUpplementation with FOlate, vitamin B6 and B12 and/or OMega-3 fatty acids
Syst-Eur
: Systolic Hypertension in Europe
TNT
: Treating to New Targets
UKPDS
: United Kingdom Prospective Diabetes Study
VADT
: Veterans Affairs Diabetes Trial
VALUE
: Valsartan Antihypertensive Long-term Use
VITATOPS
: VITAmins TO Prevent Stroke
VLDL
: very low-density lipoprotein
WHO
: World Health Organization

### 1.1 Introduction

Atherosclerotic cardiovascular disease (CVD) is a chronic disorder developing insidiously throughout life and usually progressing to an advanced stage by the time symptoms occur. It remains the major cause of premature death in Europe, even though CVD mortality has …

/pdf/european-guidelines-on-cardiovascular-disease-prevention-in-4z4nw4311i.pdf

European Guidelines on Cardiovascular Disease Prevention in Clinical Practice (Version 2012)

Suicidal Ideation Attributes Scale

Prescribing, Aspiring, Empowering and Hedging: How Australia’s state governments legislate community engagement at the local government level

PB is supported by NHMRC Early Career Fellowship 1035262, HC by NHMRC Fellowship 1056964 and KG by NHMRC Fellowship 1059620. This research was funded by the Mental Health Advisory Board of the Commonwealth Department of Health and Ageing. The authors alone are responsible for the content and writing of the paper.

State-based output in Australian mental health research.

Despite the range of studies examining the relationship between mental health and social media data, not all prior studies have validated the social media markers against “ground truth”, or validated psychiatric information, in general community samples. Instead, researchers have approximated psychiatric diagnosis using user statements such as “I have been diagnosed as X”. Without “ground truth”, the value of predictive algorithms is highly questionable and potentially harmful. In addition, for social media data, whilst linguistic features have been widely identified as strong markers of mental health disorders, little is known about non-textual features on their links with the disorders. The current work is a longitudinal study during which participants’ mental health data, consisting of depression and anxiety scores, were collected fortnightly with a validated, diagnostic, clinical measure. Also, datasets with labels relevant to mental health scores, such as emotional scores, are also employed to improve the performance in prediction of mental health scores. This work introduces a deep neural network-based method integrating sub-networks on predicting affective scores and mental health outcomes from images. Experimental results have shown that in the both predictions of emotion and mental health scores, (1) deep features majorly outperform handcrafted ones and (2) the proposed network achieves better performance compared with separate networks.

Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web

Erratum: Memory complaints in a community sample aged 60-64 years: Associations with cognitive functioning, psychiatric symptoms, medical conditions, APOE genotype, hippocampus and amygdala volumes, and white-matter hyperintensities (Psychological Medicine (2004) vol. 34 (1495-1506))

Helen Christensen

Papers

Suicidal Ideation Attributes Scale

Prescribing, Aspiring, Empowering and Hedging: How Australia’s state governments legislate community engagement at the local government level

State-based output in Australian mental health research.

Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web

Erratum: Memory complaints in a community sample aged 60-64 years: Associations with cognitive functioning, psychiatric symptoms, medical conditions, APOE genotype, hippocampus and amygdala volumes, and white-matter hyperintensities (Psychological Medicine (2004) vol. 34 (1495-1506))