The handling and processing of data in a clinical chemical laboratory

Peer Reviewe

eHealth Applications in Health Care Management

© 2002 Svensson; licensee BioMed Central Ltd. This article is published in Open Access: verbatim copying and redistribution of this article are permitted in all media for any non-commercial purpose, provided this notice is preserved along with the article's original URL

Teledermatology in Hong Kong: a cost effective method to provide service to the elderly patients living in instiututions

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Zimbabwean diabetics' beliefs about health and illness: an interview study

<p>Abstract</p> <p>Background</p> <p>Diabetes mellitus (DM) is increasing globally, with the greatest increase in Africa and Asia. In Zimbabwe a threefold increase was shown in the 1990s. Health-related behaviour is important in maintaining health and is determined by individual beliefs about health and illness but has seen little study. The purpose of the study was to explore beliefs about health and illness that might affect self-care practice and health care seeking behaviour in persons diagnosed with DM, living in Zimbabwe.</p> <p>Methods</p> <p>Exploratory study. Consecutive sample from a diabetes clinic at a central hospital. Semi-structured interviews were held with 21 persons aged 19-65 years. Data were analysed using qualitative content analysis.</p> <p>Results</p> <p>Health was described as freedom from disease and well-being, and individual factors such as compliance with advice received and drugs were considered important to promote health. A mixture of causes of DM, predominantly individual factors such as heredity, overweight and wrong diet in combination with supernatural factors such as fate, punishment from God and witchcraft were mentioned. Most respondents did not recognize the symptoms of DM when falling ill but related the problems to other diseases, e.g. HIV, malaria etc. Limited knowledge about DM and the body was indicated. Poor economy was mentioned as harmful to health and a consequence of DM because the need to buy expensive drugs, food and attend check-ups. Self-care was used to a limited extent but if used, a combination of individual measures, household remedies or herbs and religious acts such as prayers and holy water were frequently used, and in some cases health care professionals were consulted.</p> <p>Conclusions</p> <p>Limited knowledge about DM, based on beliefs about health and illness including biomedical and traditional explanations related to the influence of supernatural forces, e.g. fate, God etc., were found, which affected patients' self-care and care-seeking behaviour. Strained economy was stated to be a factor of the utmost importance affecting the management of DM and thus health. To develop cost-effective and optimal diabetes care in a country with limited resources, not only educational efforts based on individual beliefs are needed but also considering systemic and structural conditions in order to promote health and to prevent costly consequences of DM.</p

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap with arXiv:1912.0313