18 research outputs found

    Development of temporal logic-based fuzzy decision support system for diagnosis of acute rheumatic fever/rheumatic heart disease

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    In this paper we describe our research work in developing a Clinical Decision Support System (CDSS) for the diagnosis of Acute Rheumatic Fever (ARF)/Rheumatic Heart Diseases (RHD) in Nepal. This paper expressively emphasizes the three problems which have previously not been addressed, which are: (a) ARF in Nepal has created a lot of confusion in the diagnosis and treatment, due to the lack of standard unique procedures, (b) the adoption of foreign guideline is not effective and does not meet the Nepali environment and lifestyle, (c) using (our proposed method) of hybrid methodologies (knowledge-based, temporal theory and Fuzzy logic) together to design and develop a system to diagnose of ARF case an early stage in the English and Nepali version. The three tier architecture is constructed by integrating the MS Access for backend and C#.net for fronted to deployment of the system

    A Conceptual Framework to Predict Mental Health Patients' Zoning Classification.

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    Zoning classification is a rating mechanism, which uses a three-tier color coding to indicate perceived risk from the patients' conditions. It is a widely adopted manual system used across mental health settings, however it is time consuming and costly. We propose to automate classification, by adopting a hybrid approach, which combines Temporal Abstraction to capture the temporal relationship between symptoms and patients' behaviors, Natural Language Processing to quantify statistical information from patient notes, and Supervised Machine Learning Models to make a final prediction of zoning classification for mental health patients

    Development of decision support system for the diagnosis of arthritis pain for rheumatic fever patients: Based on the fuzzy approach

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    Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules

    Rising rural body-mass index is the main driver of the global obesity epidemic in adults

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    Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

    Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

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    Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5‚Äď19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9‚Äď10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3¬∑5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes‚ÄĒgaining too little height, too much weight for their height compared with children in other countries, or both‚ÄĒoccurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

    Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)