Search results for: backward chaining inference

Authors: Daniel Puciato, Piotr Oleśniewicz, Julita Markiewicz-Patkowska, Krzysztof Widawski, Michał Rozpara, Władysław Mynarski, Agnieszka Gawlik, Małgorzata Dębska, Soňa Jandová

Abstract:

Physical activity as a part of people’s everyday life reduces the risk of many diseases, including those induced by lifestyle, e.g. obesity, type 2 diabetes, osteoporosis, coronary heart disease, degenerative arthritis, and certain types of cancer. That refers particularly to professionally active people, including the early senior group working on non-manual positions. The aim of the study is to evaluate the relationship between physical activity and the socio-economic status of non-manual workers from Wroclaw—one of the biggest cities in Poland, a model setting for such investigations in this part of Europe. The crucial problem in the research is to find out the percentage of respondents who meet the health-related recommendations of the World Health Organization (WHO) concerning the volume, frequency, and intensity of physical activity, as well as to establish if the most important socio-economic factors, such as gender, age, education, marital status, per capita income, savings and debt, determine the compliance with the WHO physical activity recommendations. During the research, conducted in 2013, 1,170 people (611 women and 559 men) aged 21–60 years were examined. A diagnostic poll method was applied to collect the data. Physical activity was measured with the use of the short form of the International Physical Activity Questionnaire with extended socio-demographic questions, i.e. concerning gender, age, education, marital status, income, savings or debts. To evaluate the relationship between physical activity and selected socio-economic factors, logistic regression was used (odds ratio statistics). Statistical inference was conducted on the adopted ex ante probability level of p<0.05. The majority of respondents met the volume of physical effort recommended for health benefits. It was particularly noticeable in the case of the examined men. The probability of compliance with the WHO physical activity recommendations was highest for workers aged 21–30 years with secondary or higher education who were single, received highest incomes and had savings. The results indicate the relations between physical activity and socio-economic status in the examined women and men. People with lower socio-economic status (e.g. manual workers) are physically active primarily at work, whereas those better educated and wealthier implement physical effort primarily in their leisure time. Among the investigated subjects, the youngest group of non-manual workers have the best chances to meet the WHO standards of physical activity. The study also confirms that secondary education has a positive effect on the public awareness on the role of physical activity in human life. In general, the analysis of the research indicates that there is a relationship between physical activity and some socio-economic factors of the respondents, such as gender, age, education, marital status, income per capita, and the possession of savings. Although the obtained results cannot be applied for the general population, they show some important trends that will be verified in subsequent studies conducted by the authors of the paper.

Keywords: IPAQ, nonmanual workers, physical activity, socioeconomic factors, WHO

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Authors: Marcela Davalos Lopez

Abstract:

The history of the circulation of hygienic knowledge and the consolidation of public health in Latin American cities towards the end of the 19th century is well known. Among them, Mexico City was inserted in international politics, strengthened institutions, medical knowledge, applied parameters of modernity and built sanitary engineering works. Despite the power that this hygienist system achieved, its scope was relative: it cannot be generalized to all cities. From a comparative and contextual analysis, it will be shown that conclusions derived from modern urban historiography present, from our contemporary observations, fractures. Between 1890 and 1930, the small cities and areas surrounding the Mexican capital adapted in their own way the international and federal public health regulations. This will be shown for neighborhoods located around Mexico City and in a medium city, close to the Mexican capital (about 80 km), called Cuernavaca. While the inhabitants of the neighborhoods kept awaiting the evolutionary process and the forms that public hygiene policies were taking (because they were witnesses and affected in their territories), in Cuernavaca, the dictates came as an echo. While the capital was drained, large roads were opened, roundabouts were erected, residents were expelled, and drains, sewers, drinking water pipes, etc., were built; Cuernavaca was sheltered in other times and practices. What was this due to? Undoubtedly, the time and energy that it took politicians and the group of "scientists" to carry out these enormous works in the Mexican capital took them away from addressing the issue in remote villages. It was not until the 20th century that the federal hygiene policy began to be strengthened. Despite this, there are other factors that emphasize the particularities of each site. I would like to draw attention here to the different receptions that each town prepared on public hygiene. We will see that Cuernavaca responded to its own semi-rural culture, history, orography and functions, prolonging for much longer, for example, the use of its deep ravines as sewers. For their part, the neighborhoods surrounding the capital, although affected and excluded from hygienist policies, chose to move away from them and solve the deficiencies with their own resources (they resorted to the waste that was left from the dried lake of Mexico to continue their lake practices). All of this points to a paradox that shapes our contemporary concerns: on the one hand, the benefits derived from medical knowledge and its technological applications (in this work referring particularly to the urban health system) and, on the other, the alteration it caused in environmental settings. Places like Cuernavaca (classified by the nineteenth-century and hygienists of the first decades of the twentieth century as backward), as well as landscapes such as neighborhoods, affected by advances in sanitary engineering, keep in their memory buried practices that we observe today as possible ways to reestablish environmental balances: alternative uses of water; recycling of organic materials; local uses of fauna; various systems for breaking down excreta, and so on. In sum, what the nineteenth and first half of the twentieth centuries graduated as levels of backwardness or progress, turn out to be key information to rethink the routes of environmental conservation. When we return to the observations of the scientists, politicians and lawyers of that period, we find historically rejected cultural alterity. Populations such as Cuernavaca that, due to their history, orography and/or insufficiency of federal policies, kept different relationships with the environment, today give us clues to reorient basic elements of cities: alternative uses of water, waste of raw materials, organic or consumption of local products, among others. It is, therefore, a matter of unearthing the rejected that cries out to emerge to the surface.

Keywords: sanitary hygiene, Mexico city, cultural alterity, environmental conservation, environmental history

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Authors: Pei Yi Lin

Abstract:

Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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