Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 20 de 490
Filter
1.
Einstein (Säo Paulo) ; 22: eAO0328, 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1534330

ABSTRACT

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

2.
Journal of Clinical Hepatology ; (12): 773-781, 2024.
Article in Chinese | WPRIM | ID: wpr-1016523

ABSTRACT

ObjectiveTo investigate the differences in the risk factors for postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) between the 2005 and 2016 editions of the definition and classification standards for pancreatic fistula, and to establish a risk prediction model for pancreatic fistula based on the 2016 edition. MethodsA retrospective analysis was performed for the clinical data of 303 patients who were admitted to Tianjin Third Central Hospital and underwent PD from January 2016 to May 2022, and the patients with POPF were identified based on the new and old editions. The independent-samples t test or the non-parametric Mann-Whitney U test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups. The univariate and multivariate logistic regression analyses were used to investigate the differences in the risk factors for pancreatic fistula after PD between the two editions; a risk prediction model was established for POPF based on the 2016 edition, and the receiver operating characteristic curve was used to invesitgate the accuracy of this model in predicting POPF and perform model validation. ResultsAccording to the 2005 edition, the univariate analysis showed that the diameter of the main pancreatic duct (χ2=31.641, P<0.001), main pancreatic duct index (χ2=52.777, P<0.001), portal vein invasion (χ2=6.259, P=0.012), intra-abdominal fat thickness (χ2=7.665, P=0.006), preoperative biliary drainage (χ2=5.999, P=0.014), pancreatic cancer (χ2=5.544, P=0.019), marginal pancreatic thickness (t=2.055, P=0.032), pancreatic CT value (t=-3.224, P=0.002), and preoperative blood amylase level (Z=-2.099, P=0.036) were closely associated with POPF, and the multivariate logistic regression analysis showed that main pancreatic duct index (odds ratio [OR]=0.000, 95% confidence interval [CI]: 0.000‍ ‍—‍ ‍0.011, P<0.05), pancreatic cancer (OR=4.843, 95%CI: 1.285‍ ‍—‍ ‍18.254, P<0.05), and pancreatic CT value (OR=0.869, 95%CI: 0.806‍ ‍—‍ ‍0.937, P<0.05) were independent risk factors; based on the 2016 edition, the univariate analysis showed the diameter of the main pancreatic duct (χ2=5.391, P=0.020), main pancreatic duct index (χ2=11.394, P=0.001), intra-abdominal fat thickness (χ2=8.899, P=0.003), marginal pancreatic thickness (t=2.665, P=0.009), pancreatic CT value (t=-2.835, P=0.004) were closely associated with POPF, and the multivariate logistic regression analysis showed that main pancreatic duct index (OR=0.001, 95%CI: 0.000‍ ‍—‍ ‍0.050, P<0.05) and pancreatic CT value (OR=0.943, 95%CI: 0.894‍ ‍—‍ ‍0.994, P<0.05) were independent risk factors. A risk prediction model was established for POPF after PD, and the ROC curve analysis showed that this model had an area under the ROC curve of 0.788 (95%CI: 0.707‍ ‍—‍ ‍0.870) in the modeling group and 0.804 (95%CI: 0.675‍ ‍—‍ ‍0.932) in the validation group. ConclusionMain pancreatic duct index and pancreatic CT value are closely associated with POPF after PD, and the risk prediction model for pancreatic fistula based on the 2016 edition has a good prediction accuracy.

3.
Journal of Clinical Hepatology ; (12): 726-733, 2024.
Article in Chinese | WPRIM | ID: wpr-1016516

ABSTRACT

ObjectiveTo investigate the prevalence of liver cirrhosis and the changing trend of the disease burden of liver cirrhosis in the Chinese population from 1990 to 2019, and to provide a data reference for formulating the prevention and treatment strategies for liver cirrhosis in China. MethodsThe Global Burden of Disease Study 2019 was used to collect the data on the incidence rate, mortality rate, disability-adjusted life years (DALY), years of life lost (YLL), and years lived with disability (YLD) of liver cirrhosis. The Joinpoint regression model was used to analyze the changing trend of disease burden; the age-period-cohort (APC) model was used to evaluate age, period, and cohort effects; R software BAPC package was used to predict future changes in disease burden. ResultsFrom 1990 to 2019 in China, there were increases in the numbers of liver cirrhosis cases and prevalent cases in the general population, as well as in the male and female populations, while there was a reduction in the number of deaths. From 1990 to 2019, the standardized incidence rate, standardized prevalence rate, and standardized mortality rate of liver cirrhosis in the Chinese population showed a downward trend, with a mean annual reduction of 0.62% (95% confidence interval [CI]: -0.74% to -0.50%, t=9.99, P<0.001), 0.44% (95%CI: -0.53% to -0.35%, t=13.18, P<0.001), and 3.02% (95%CI: -3.12% to -2.93%, t=7.58, P<0.001), respectively. From 1990 to 2019, the standardized DALY, YLL, and YLD rates of liver cirrhosis in the Chinese population also showed a downward trend, with a mean annual reduction of 3.27% (95%CI: -3.37% to -3.18%, t=6.22, P<0.001), 3.32% (95%CI: -3.42% to -3.22%, t=9.31, P<0.001), and 1.42% (95%CI: -1.49% to -1.34%, t=4.93, P<0.001), respectively. From 1990 to 2019, the incidence rate of liver cirrhosis in the Chinese population first increased and then decreased with age, while the mortality rate showed an increasing trend, and the risks of disease onset and death showed a decreasing trend with time and birth cohort. The predictive model showed that the standardized incidence rate, prevalence rate, mortality rate, and DALY rate of liver cirrhosis in China will show a decreasing trend from 2020 to 2030. Alcohol was the most important risk factor for both male and female populations, followed by medication. ConclusionThe disease burden of liver cirrhosis in China shows a decreasing trend from 1990 to 2019, with sex and age differences, especially in the middle-aged male population. Effective measures should be taken for intervention.

4.
Cienc. act. fís. (Talca, En línea) ; 24(2)dic. 2023. tab, ilus, graf
Article in Spanish | LILACS | ID: biblio-1528268

ABSTRACT

El objetivo del presente trabajo es analizar el desempeño deportivo de la delegación chilena en los Juegos Panamericanos celebrados entre los años 1951 y 2023, haciendo uso de datos retrospectivos y proyectivos a través de series temporales de tiempo. Para esto se empleó un diseño cuantitativo, no experimental y longitudinal de tendencias y un método de suavización exponencial simple, que utiliza promedios históricos y que permite realizar una predicción o comportamiento futuro basado en una media ponderada de los valores actuales y de los pasados. A partir de los resultados obtenidos, fue posible concluir que, en las últimas décadas, la ubicación de Chile en el ranking de los Juegos Panamericanos se ha estabilizado en torno a un onceavo lugar, posición pronosticada para Santiago 2023. Manteniéndose condiciones similares, el desempeño deportivo general y específico no tendría un quiebre exponencial de la tendencia y los resultados no resultan favorables, específicamente en lo que respecta a la obtención de medallas de oro y la posición general de la delegación.


The objective of this paper is to analyze the sports performance of the Chilean delegation in the Pan American Games held between 1951 and 2023, using retrospective and projective data through time series. For this purpose, a quantitative, non-experimental and longitudinal design of trends and a simple exponential smoothing method was used, which uses historical averages and allows a prediction or future behavior based on a weighted average of current and past values. From the results obtained, it was possible to conclude that, in recent decades, Chile's position in the Pan American Games ranking has stabilized around eleventh place, a position predicted for Santiago 2023. Maintaining similar conditions, the general and specific sporting performance would not have an exponential break in the trend and the results are not favorable, specifically in terms of obtaining gold medals and the overall position of the delegation.


O objetivo deste artigo é analisar o desempenho esportivo da delegação chilena nos Jogos Pan-Americanos realizados entre 1951 e 2023, usando dados retrospectivos e projetivos por meio de séries temporais. Para isso, foi utilizado um desenho quantitativo, não experimental e longitudinal de tendências e um método de suavização exponencial simples, que utiliza médias históricas e permite uma previsão do comportamento futuro com base em uma média ponderada dos valores atuais e passados. Com base nos resultados obtidos, foi possível concluir que, nas últimas décadas, a posição do Chile no ranking dos Jogos Pan-Americanos se estabilizou em torno do 11º lugar, posição prevista para Santiago 2023. Mantendo-se condições semelhantes, o desempenho esportivo geral e específico não teria uma quebra exponencial na tendência e os resultados não são favoráveis, especificamente em termos de conquista de medalhas de ouro e posição geral da delegação.


Subject(s)
Humans , Male , Female , History, 20th Century , History, 21st Century , Sports/history , Models, Statistical , Chile
5.
Rev. latinoam. enferm. (Online) ; 31: e4079, Jan.-Dec. 2023. tab, graf
Article in Spanish | LILACS, BDENF | ID: biblio-1530188

ABSTRACT

Objetivo: analizar el patrón temporal y estimar las tasas de mortalidad en las primeras 24 horas de vida y por causas evitables en el estado de Pernambuco en el período de 2000 a 2021. Método: estudio ecológico, teniendo como unidad de análisis el trimestre. La fuente de datos se constituyó por el Sistema de Informaciones sobre Mortalidad y el Sistema de Informaciones sobre Nacidos Vivos. El modelado de series temporales se realizó según el Modelo Autorregresivo Integrado de Promedio Móvil. Resultados: se registraron 14.462 óbitos en las primeras 24 horas de vida, siendo 11.110 (el 76,8%) evitables. Se observa para los pronósticos ( forecasts) que la tasa de mortalidad en las primeras 24 horas de vida registro una variación de 3,3 a 2,4 por 1.000 nacidos vivos, y la tasa de mortalidad por causas evitables de 2,3 a 1,8 por 1.000 nacidos vivos. Conclusión: la predicción sugirió avances en la reducción de la mortalidad en las primeras 24 horas de vida en el estado y por causas evitables. Los modelos ARIMA presentaron estimaciones satisfactorias para las tasas de mortalidad y por causas evitables en las primeras 24 horas de vida.


Objective: to analyze the temporal pattern and estimate mortality rates in the first 24 hours of life and from preventable causes in the state of Pernambuco from 2000 to 2021. Method: an ecological study, using the quarter as the unit of analysis. The data source was made up of the Mortality Information System and the Live Birth Information System. The time series modeling was conducted according to the Autoregressive Integrated Moving Average Model. Results: 14,462 deaths were recorded in the first 24 hours of life, 11,110 (76.8%) of which being preventable. It is observed from the forecasts that the mortality rate in the first 24 hours of life ranged from 3.3 to 2.4 per 1,000 live births, and the mortality rate from preventable causes ranged from 2.3 to 1.8 per 1,000 live births. Conclusion: the prediction suggested progress in reducing mortality in the first 24 hours of life in the state and from preventable causes. The ARIMA models presented satisfactory estimates for mortality rates and preventable causes in the first 24 hours of life.


Objetivo: analisar o padrão temporal e estimar as taxas de mortalidade nas primeiras 24 horas de vida e por causas evitáveis no estado de Pernambuco no período de 2000 a 2021. Método: estudo ecológico, tendo como unidade de análise o trimestre. A fonte de dados foi constituída pelo Sistema de Informações sobre Mortalidade e pelo Sistema de Informações sobre Nascidos Vivos. A modelagem da série temporal foi conduzida segundo o Modelo Autorregressivo Integrado de Médias Móveis. Resultados: foram registrados 14.462 óbitos nas primeiras 24 horas de vida, sendo 11.110 (76,8%) evitáveis. Observa-se para os forecasts que a taxa de mortalidade nas primeiras 24 horas de vida variou de 3,3 a 2,4 por 1.000 nascidos vivos, e a taxa de mortalidade por causas evitáveis variou de 2,3 a 1,8 por 1.000 nascidos vivos. Conclusão: a previsão sugeriu avanços na redução da mortalidade nas primeiras 24 horas de vida no estado e por causas evitáveis. Os modelos ARIMA apresentaram estimativas satisfatórias para as taxas de mortalidade e por causas evitáveis nas primeiras 24 horas de vida.


Subject(s)
Humans , Infant, Newborn , Brazil , Information Systems , Mortality , Cause of Death
6.
Medisan ; 27(6)dic. 2023. tab
Article in Spanish | LILACS, CUMED | ID: biblio-1534914

ABSTRACT

Introducción: Las enfermedades cardiovasculares constituyen la primera causa de muerte en el mundo, por lo que la identificación y modificación de los factores de riesgo asociados a ellas constituyen estrategias priorizadas por la Organización Mundial de la Salud. Contar con un modelo de predicción del riesgo cardiovascular enriquecido con la evaluación de la disfunción endotelial influiría positivamente en estas metas. Objetivos: Identificar la presencia de disfunción endotelial en pacientes con enfermedades cardiovasculares o sin estas y determinar la asociación entre ambas. Métodos: Se realizó un estudio observacional y descriptivo, de serie de casos, en el Centro de Cardiología y Cirugía Cardiovascular del Hospital Provincial Docente Clínico-Quirúrgico Saturnino Lora de Santiago de Cuba, desde enero del 2022 hasta igual mes del 2023, donde se analizaron como variables los factores de riesgo cardiovascular tradicionales y los biomarcadores de disfunción endotelial. Secundariamente, se llevó a cabo un estudio analítico de casos y controles en el cual se aplicó la regresión logística binaria multivariada. Resultados: Se confirmó la presencia de disfunción endotelial asociada a la aparición de las enfermedades cardiovasculares, lo que se evaluó a través del índice de vasodilatación, mediado por el flujo de la arteria braquial y las concentraciones plasmáticas de fibrinógeno. Conclusiones: Las características epidemiológicas y clínicas de los pacientes con enfermedades cardiovasculares o sin estas no difirieron de lo registrado en la literatura especializada acerca de la base de identificación de los factores de riesgo tradicionales.


Introduction: Cardiovascular diseases constitute the first death cause worldwide, reason why the identification and modification of associated risk factors constitute prioritized strategies by the World Health Organization. To have a prediction model of cardiovascular risk enriched with the evaluation of the endothelial dysfunction would influence positively in these goals. Objectives: To identify the presence of endothelial dysfunction in patients with or without cardiovascular diseases and to determine the association between them. Methods: An observational and descriptive cases series study was carried out in the Cardiology and Cardiovascular Surgery Center at Saturnino Lora Teaching Clinical Surgical Provincial Hospital in Santiago de Cuba, from January, 2022 to the same month, 2023, where the traditional cardiovascular risk factors and endothelial dysfunction biomarkers were analyzed as variables. Secondarily, an analytic case-control study was carried out in which multivariate binary logistic regression was applied. Results: The presence of endothelial dysfunction associated with the onset of cardiovascular diseases was confirmed, what was evaluated through the vasodilatation index, mediated by the brachial artery flow and the fibrinogen plasmatic concentrations. Conclusions: The clinical and epidemiological pattern of patients with or without cardiovascular diseases did not differ from that reported in the specialized literature on the base of the identification of traditional risk factors.

7.
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1535453

ABSTRACT

Introducción: Los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: Identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: Se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: Se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron 16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial, seguido de Máquinas de Vectores de Soporte. Conclusiones: En la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.


Introduction: Machine learning methods allow to manipulate structured and unstructured data to build predictive models and support decision-making. Objective: To identify machine learning methods applied to predict the epidemiological behavior of vector-borne diseases using epidemiological surveillance data. Methodology: A literature search in EMBASE and PubMed, bibliometric analysis, and information synthesis were performed. Results: A total of 41 papers were selected, all of them were published in the last decade. The most frequent keyword was dengue. Most authors (88.3 %) participated in a research article. Sixteen machine learning methods were found, the most frequent being Artificial Neural Network, followed by Support Vector Machines. Conclusions: In the last decade there has been an increase in the number of articles that aim to predict the epidemiological behavior of vector-borne diseases using by means of various machine learning methods that incorporate time series of cases, climatological variables, and other sources of open data information.

8.
Medicina (B.Aires) ; 83(4): 558-568, ago. 2023. graf
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1514514

ABSTRACT

Resumen Introducción : Los modelos epidemiológicos han sido ampliamente utilizados durante la pandemia de COVID-19, aunque la evaluación de su desempeño ha sido limitada. El objetivo del presente trabajo fue evaluar de forma retrospectiva un modelo SEIR para la predicción de casos a corto plazo (1 a 3 semanas), cuantificando su desempeño real y potencial, me diante la optimización de los parámetros del modelo. Métodos : Se realizaron proyecciones para cada día de la primera ola de casos (31 de julio de 2020 al 11 de marzo de 2021) en el municipio de General Pueyrredón (Argentina), cuantificando el desempeño del modelo en términos de incertidumbre, inexactitud e imprecisión. La evaluación se realizó con los parámetros originales del modelo (utilizados en proyecciones que fueron oportunamente publicadas), y luego variando distintos parámetros a fin de identificar valores óptimos. Resultados : El análisis del desempeño del modelo mostró que valores alternativos de algunos parámetros, y la corrección de los valores de entrada utilizando un filtro de "media móvil" para eliminar las variaciones semanales en los reportes de casos, habrían otorgado mejores resultados. El modelo con los parámetros opti mizados logró disminuir desde casi 40% a menos de 15% la incertidumbre, con valores similares de inexactitud, y con una imprecisión levemente mayor. Discusión : Modelos epidemiológicos sencillos, sin grandes requerimientos para su implementación, pue den ser de utilidad para la toma de decisiones rápi das en localidades pequeñas o con recursos limitados, siempre y cuando se tenga en cuenta la importancia de su evaluación y la consideración de sus alcances y limitaciones.


Abstract Introduction : Epidemiological models have been widely used during the COVID-19 pandemic, although performance evaluation has been limited. The objec tive of this work was to thoroughly evaluate a SEIR model used for the short-term (1 to 3 weeks) predic tion of cases, quantifying its actual past performance, and its potential performance by optimizing the model parameters. Methods : Daily case forecasts were obtained for the first wave of cases (July 31, 2020 to March 11, 2021) in the district of General Pueyrredón (Argentina), quantifying the model performance in terms of uncertainty, inac curacy and imprecision. The evaluation was carried out with the original parameters of the model (used in the forecasts that were published), and also varying different parameters in order to identify optimal values. Results : The analysis of the model performance showed that alternative values of some parameters, and the correction of the input values using a "mov ing average" filter to eliminate the weekly variations in the case reports, would have yielded better results. The model with the optimized parameters was able to reduce the uncertainty from almost 40% to less than 15%, with similar values of inaccuracy, and with slightly greater imprecision. Discussion : Simple epidemiological models, without large requirements for their implementation, can be very useful for making quick decisions in small cities or cities with limited resources, as long as the importance of their evaluation is taken into account and their scope and limitations are considered.

9.
Biomédica (Bogotá) ; 43(Supl. 1)ago. 2023.
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1550064

ABSTRACT

Introducción. La diabetes es una enfermedad crónica que se caracteriza por el aumento de la concentración de la glucosa en sangre. Puede generar complicaciones que afectan la calidad de vida y aumentan los costos de la atención en salud. En los últimos años, las tasas de prevalencia y mortalidad han aumentado en todo el mundo. El desarrollo de modelos con gran desempeño predictivo puede ayudar en la identificación temprana de la enfermedad. Objetivo. Desarrollar un modelo basado en la inteligencia artificial para apoyar la toma de decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó a cabo un estudio de corte transversal, utilizando un conjunto de datos que incluía edad, signos y síntomas de pacientes con diabetes y de individuos sanos. Se utilizaron técnicas de preprocesamiento para los datos. Posteriormente, se construyó el modelo basado en mapas cognitivos difusos. El rendimiento se evaluó mediante tres parámetros: exactitud, especificidad y sensibilidad. Resultados. El modelo desarrollado obtuvo un excelente desempeño predictivo, con una exactitud del 95 %. Además, permitió identificar el comportamiento de las variables involucradas usando iteraciones simuladas, lo que proporcionó información valiosa sobre la dinámica de los factores de riesgo asociados con la diabetes. Conclusiones. Los mapas cognitivos difusos demostraron ser de gran valor para la identificación temprana de la enfermedad y en la toma de decisiones clínicas. Los resultados sugieren el potencial de estos enfoques en aplicaciones clínicas relacionadas con la diabetes y respaldan su utilidad en la práctica médica para mejorar los resultados de los pacientes.


Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decision-making in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

10.
Medisur ; 21(3)jun. 2023.
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1448661

ABSTRACT

Fundamento las autolesiones no suicidas se consideran un problema de salud pública y social durante la última década, el cual afecta en mayor medida a los adolescentes. La ansiedad generalizada y el bullying pueden ser factores desencadenantes para su desarrollo. Objetivo analizar un modelo explicativo de la ansiedad generalizada y el bullying como predictores de autolesiones no suicidas en adolescentes peruanos. Métodos estudio de diseño explicativo, transversal, con participación de 1 249 adolescentes peruanos, de edad promedio de 15 años (desviación estándar = 1,49) quienes respondieron escalas sobre ansiedad generalizada, bullying y autolesiones no suicidas. Para el análisis de datos, se aplicó la potencia estadística, la correlación y un modelo de regresión estructural basado en covarianzas para confirmar el modelo explicativo. Resultados las variables psicológicas se correlacionaron de manera positiva y estadísticamente significativa. El modelo propuesto presentó índices de ajuste adecuados (CFI = 0,94; RMSEA = 0,03 [IC del 90 %: 0,02-0,03] y SRMR = 0,04) y se evidenció que la ansiedad generalizada (β = 0,26, p = 0,001) y las dimensiones del bullying, como la agresión (β = 0,25, p = 0,001) y la victimización (β = 0,21, p = 0,003) predijeron de manera estadísticamente significativa las autolesiones no suicidas. Conclusiones los hallazgos sugieren que tanto la ansiedad generalizada como el bullying predicen las autolesiones no suicidas en adolescentes. La evidencia proporciona información útil para desarrollar y evaluar programas de prevención basados en estas variables psicológicas, con vistas a disminuir el riesgo de las autolesiones no suicidas.


Background non-suicidal self-harm has been considered a public and social health problem during the last decade, which affects adolescents to a greater extent. Generalized anxiety and bullying can be trigger factors for its development. Objective to analyze a generalized anxiety and bullying explanatory model as non-suicidal self-harm predictors in Peruvian adolescents. Methods cross-sectional, explanatory design study, with 1,249 Peruvian adolescents, average age 15 years old (standard deviation = 1.49), who answered scales on generalized anxiety, bullying, and non-suicidal self-harm. For data analysis, statistical power, correlation, and a structural regression model based on covariances were applied to confirm the explanatory model. Results the psychological variables were positively and statistically significantly correlated. The proposed model had adequate fit indices (CFI = 0.94; RMSEA = 0.03 [90% CI: 0.02-0.03] and SRMR = 0.04) and it was evidenced that generalized anxiety (β = 0.26, p = 0.001) and bullying dimensions such as aggression (β = 0.25, p = 0.001) and victimization (β = 0.21, p = 0.003) statistically significantly predicted self-harm not suicidal. Conclusions The findings suggest that both generalized anxiety and bullying predict non suicidal self-harm in adolescents. The evidence provides useful information for developing and evaluating prevention programs based on these psychological variables, to reduce the non-suicidal self-harm risks.

11.
Article | IMSEAR | ID: sea-219406

ABSTRACT

Aims: To evaluate interaction of soil pH and acidity with weather on Rice Brown spot (BS) occurrence in rice lowlands. Study Design: Cross sectional study. Place and Duration of Study: Four distinct rice lowlands belonging to different climatic zones (forest, transitional and savanna) of Côte d’Ivoire during cropping seasons of 2021. Methodology: BS characterization were done in different farmer fields where soil samples were also collected during dry and rainy seasons. Soil silicon and acidity were determined in those samples and rice grain yield at harvest time were recorded in different sites. Weather data related to sites and seasons were used to find out correlations. Results: Occurrence of BS was found in forest zones with scores of 4 and 3 compared to 1 and 2 in savanna and transitional zones, respectively, with seasonal variation. Both rice production and the occurrence of BS were explained by soil parameters in conjunction with climatic parameters. Rainfall (R=0.38) and relative humidity (R=0.64) leaded BS occurrence and decrease of yield. Wind speed (R=0.62) and air maximum temperature (R= 0.63) were the determinant factors affecting rice yields. Si was found to be a component of sustainable soil management that interferes with soil pH in all climatic zones. Combined with Temperature, both soil parameters predicted BS occurrence over 50%. Conclusion: Temperature decrease BS pathogens occurrence whereas high humidity increases its spread. Those parameters combined with silicon which interferes with pH could leads sustainable solutions in BS control. Furthermore, having a deep understanding with rice varietal considerations can significantly improve strategies related to rice cultivation and protection.

12.
Braz. dent. sci ; 26(3): 1-5, 2023. ilus
Article in English | LILACS, BBO | ID: biblio-1511707

ABSTRACT

A integração de geradores de texto de inteligência artificial (IA) em relatórios científicos exige uma avaliação cuidadosa de considerações éticas específicas. Embora essas tecnologias de IA ofereçam suporte à geração de texto, abordar as implicações éticas é fundamental. Este editorial destaca a necessidade de uma abordagem ponderada e responsável, enfatizando o estabelecimento de diretrizes e melhores práticas por parte de pesquisadores e comunidades científicas. Esforços colaborativos entre desenvolvedores de IA, pesquisadores e comitês éticos podem garantir a integração perfeita das tecnologias de IA, ao mesmo tempo em que mantêm a integridade, qualidade e padrões éticos da divulgação científica. Este texto oferece um resumo abrangente considerações-chave ao se utilizar geradores de texto de inteligência artificial em relatórios científicos (AU)


The integration of artificial intelligence (AI) text generators in scientific reports demands careful evaluation of specific ethical considerations. While these AI technologies offer text generation support, addressing the ethical implications is vital. This editorial highlights the need for a thoughtful and responsible approach, emphasizing the establishment of guidelines and best practices by researchers and scientific communities. Collaborative efforts between AI developers, researchers, and ethical committees can ensure the seamless integration of AI technologies while upholding the integrity, quality, and ethical standards of scientific reporting. This text comprehensively summarizes the key considerations to be followed when utilizing artificial intelligence text generators in scientific reports.(AU)


Subject(s)
Societies, Dental , Artificial Intelligence , Ethics
13.
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1533681

ABSTRACT

Introducción: La incidencia del síndrome bajo gasto cardíaco postoperatorio es variable en las distintas series publicadas, desde 4 % hasta 15 %, con una mortalidad que se aproxima al 20 %. Si bien en enfermos mayores de 70 años el síndrome puede estar presente hasta en un 63 %, a pesar del desarrollo de mejores técnicas de cardioprotección y cuidados postoperatorios, la incidencia de este síndrome en poblaciones de alto riesgo no se ha modificado en una proporción significativa. Objetivo: Diseñar y validar un modelo predictivo de síndrome de bajo gasto cardíaco postoperatorio a través de factores de riesgo. Métodos: Se realizó un estudio analítico de casos y controles en pacientes con síndrome de bajo gasto cardíaco posoperatorio, atendidos en el Centro de Cardiología y Cirugía Cardiovascular del Hospital Provincial Docente Saturnino Lora de la provincia Santiago de Cuba, en un periodo 2019-2021. Se empleó la regresión logística con ajustes para obtener el modelo. Resultados: Los factores de riesgo predictores de mayor valor fue edad > 65 años, función de ventrículo derecho disminuida, el tiempo de pinzamiento aórtico, sangrado postoperatorio que fueron los que arrojó el modelo de regresión logística. Se realizó su validación interna por división de datos. Conclusiones: El modelo predictivo elaborado a partir de la regresión logística quedó compuesto por los predictores: edad > 65 años, el tiempo de pinzamiento aórtico > 90 minutos y el sangrado posoperatorio prolongado; presentó buen ajuste y poder discriminante, sobre todo valor predictivo positivo.


Introduction: The incidence of postoperative low cardiac output syndrome is variable in the different published series, from 4% to 15%, with a mortality approaching 20%. Although in patients over 70 years of age the syndrome may be present in up to 63%, despite the development of better cardioprotection techniques and postoperative care, the incidence of this syndrome in high-risk populations has not changed in a significant proportion. Objective: To design and validate a predictive model of postoperative low cardiac output syndrome through risk factors. Methods: An analytical, case-control study was conducted in patients with postoperative low cardiac output syndrome attended at the Center for Cardiology and Cardiovascular Surgery of the Saturnino Lora Teaching Provincial Hospital in Santiago de Cuba, in the period 2019-2021. Logistic regression with adjustments to obtain the model was used. Results: The highest value predictor risk factors are: age > 65 years, decreased right ventricular function, aortic clamping time, postoperative bleeding, which were the ones that yielded the logistic regression model. Internal validation was performed by data division. Conclusions: The predictive model developed from logistic regression was composed of the predictors: age > 65 years, aortic clamping time > 90 minutes and prolonged postoperative bleeding. It presented good fit and discriminant power, especially positive predictive value.

14.
Rev. bras. enferm ; 76(6): e20220740, 2023. graf
Article in English | LILACS-Express | LILACS, BDENF | ID: biblio-1529786

ABSTRACT

ABSTRACT Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.


RESUMEN Objetivo: Desarrollar una Web App a partir de un modelo predictivo para estimar el riesgo de ingreso a la Unidad de Cuidados Intensivos (UCI) para pacientes con covid-19. Métodos: Se realizó una investigación de producción tecnológica aplicada con el desarrollo de Streamlit utilizando Python, considerando el modelo de árbol de decisiones que presentó el mejor rendimiento (AUC 0.668). Resultados: Basado en las variables asociadas con la Enfermería de Precisión, Streamlit estratifica a los pacientes ingresados en unidades clínicas que tienen más probabilidades de ser admitidos en la Unidad de Cuidados Intensivos, sirviendo como una herramienta de apoyo para la toma de decisiones para los profesionales de la salud. Consideraciones finales: El rendimiento del modelo puede haber sido influenciado por el inicio de la vacunación durante el período de recolección de datos. La Web App a través de Streamlit demostró ser una herramienta factible para presentar los resultados, debido a la facilidad de comprensión y su potencial para apoyar la toma de decisiones clínicas.


RESUMO Objetivo: Desenvolver um Web App a partir de um modelo preditivo para estimar o risco de internação de pacientes com covid-19 em UTI. Métodos: Realizou-se uma pesquisa aplicada de produção tecnológica com o desenvolvimento do Streamlit a partir do Python, considerando o modelo de árvore de decisão que apresentou o melhor desempenho (AUC 0.668). Resultados: A partir das variáveis associadas à Enfermagem de Precisão, o Streamlit estratifica os pacientes internados nas unidades clínicas com maior probabilidade de internação em Unidade de Terapia Intensiva, funcionando como uma ferramenta de apoio à tomada de decisão dos profissionais de saúde. Considerações finais: A performance do modelo pode ter sido influenciada pelo início da vacinação no período de coleta de dados, no entanto, o Web App via Streamlit mostrou-se uma ferramenta viável para a apresentação dos resultados de pesquisa, devido à facilidade de entendimento por parte dos enfermeiros e pelo potencial de apoio à decisão clínica.

15.
Rev. bras. saúde ocup ; 48: e4, 2023. tab, graf
Article in Portuguese | LILACS | ID: biblio-1431679

ABSTRACT

Resumo Introdução: realizar a predição de doenças relacionadas ao trabalho é um desafio às organizações e ao poder público. Com as técnicas de aprendizado de máquina (AM), é possível identificar fatores determinantes para a ocorrência de uma doença ocupacional, visando direcionar ações mais efetivas à proteção dos trabalhadores. Objetivo: predizer, a partir da comparação de técnicas de AM, os fatores com maior influência para a ocorrência de dermatite ocupacional. Métodos: desenvolveu-se um código em linguagem R e uma análise descritiva dos dados e identificaram-se os fatores de influência de acordo com a técnica de AM que demonstrou melhor desempenho. O banco de dados foi disponibilizado pelo Serviço de Dermatologia Ocupacional da Fundação Oswaldo Cruz e contém informações de trabalhadores que apresentaram alterações cutâneas sugestivas de dermatite ocupacional no período de 2000-2014. Resultados: as técnicas com melhor desempenho foram: neural network, random forest, support vector machine e naive Bayes. As variáveis sexo, escolaridade e profissão foram as mais adequadas para os modelos de previsão de dermatite ocupacional. Conclusão: as técnicas de AM possibilitam predizer os fatores que influenciam a segurança e a saúde dos trabalhadores, os parâmetros que subsidiam a implantação de procedimentos e as políticas mais efetivas para prevenir a dermatite ocupacional.


Abstract Introduction: to predict work related diseases is a challenge for organizations and the governmental authorities. By means of machine learning (ML) techniques it is possible to identify factors that determine the occurrence of an occupational disease, aiming at taking more effective actions to protect workers. Objective: to predict, by comparing ML techniques, the factors which highly influence the occurrence of occupational dermatitis. Methods: we developed a code in R language and a descriptive analysis of the data and identified the influence factors according to the ML technique that presented the best performance. The database was made available by the Occupational Dermatology Service of Oswaldo Cruz Foundation and assembles information of the workers who experienced cutaneous alterations suggestive of occupational dermatitis between 2000-2014. Results: the techniques which presented the best performance were: neural network, random forest, support vector machine, and naive Bayes. Sex, schooling, and profession were the most adequate variables for the occupational dermatitis prediction models. Conclusion: ML techniques allowed to predict the factors that influence the workers' safety and health, as well as the parameters that subsidize the procedures implementation, and the most effective policies to prevent occupational dermatitis.


Subject(s)
Safety , Occupational Health , Dermatitis, Occupational , Dermatology , Protective Factors , Occupational Diseases , Learning , Methods , Occupational Groups
16.
Chinese Journal of School Health ; (12): 1198-1202, 2023.
Article in Chinese | WPRIM | ID: wpr-985586

ABSTRACT

Objective@#To explore the effectiveness of machine learning algorithms in predicting non-suicidal self-injury (NSSI) behavior among college students, and to analyze the influencing factors of NSSI behavior, thus providing a reference for promoting psychological well-being.@*Methods@#In December 2022, a stratified random cluster sampling method was used to select 835 college students from a university in Guizhou Province, China. The Adolescent Self-injury Scale, Family Function Assessment Scale, and Emotion Regulation Self-efficacy Scale were used to evaluate the participants. Demographic characteristics, family factors, and emotional factors were taken as independent variables, while the dependent variable was whether college students exhibited NSSI behavior. Machine learning algorithms, including Logistic regression, support vector machine (SVM), decision trees, algorithm gradient boosting trees, random forests, and AdaBoost, were used to construct predictive models.@*Results@#The detection rate of NSSI behavior among the college students was 23.23% (194 individuals). The NSSI behavior group scored higher than the non-NSSI behavior group in total family function, emotional communication, egoism, and family rules ( t=3.02, 3.35 , 2.23,2.87, P <0.05). On the other hand, the non-NSSI behavior group scored higher than the NSSI behavior group in total emotion regulation selfefficacy, managing negative emotion self-efficacy, and expressing positive emotion self-efficacy ( t=-5.04, -5.48 , -2.43, P <0.05). The recall rates of random forests, SVM, Logistic regression, decision trees, algorithm gradient boosting trees, and AdaBoost were 84.3% , 90.6%, 73.4%, 87.5%, 95.3%, 89.0%, respectively. The F1 scores were 84.4%, 92.1%, 71.2 %, 79.4%, 91.7%, 89.1% , respectively. The respective precision rates were 84.4%, 93.5%, 69.1%, 72.7%, 88.4%, 89.1 %. The AUC scores were 0.845, 0.922, 0.706, 0.776, 0.915, and 0.891, respectively.@*Conclusion@#Compared to the algorithm gradient boosting tree, random forest, Logistic regression, and AdaBoost models, the SVM model has a better predictive effect on whether college students in Guizhou Province exhibits NSSI behavior. It is recommended to use an appropriate model to identify students at risk of NSSI behavior as early as possible and provide psychological crisis interventions to promote their mental health.

17.
Chinese Journal of Perinatal Medicine ; (12): 601-606, 2023.
Article in Chinese | WPRIM | ID: wpr-995146

ABSTRACT

Incidence of twin pregnancies increases significantly in recent years. Twin pregnancies are likely to have a higher risk of quicker progression and more severe preeclampsia (PE) than singletons, making the prediction and prevention of PE of twin pregnancies even more important. The prediction and screening for PE have evolved from guideline-based risk factor screening to simple models with maternal factors only, and then to complex models with a wider range of indicators. Besides, the modeling algorithms have expanded from logistic regression to complex algorithms such as competing risk models. Continuous improvements have been achieved in the prediction models. This paper presents a comprehensive overview of the applicability and the prospect of these models in this area in twin pregnancies and suggests that the prediction models should be improved by optimizing modeling strategies using localized indicators.

18.
Chinese Journal of Perinatal Medicine ; (12): 519-522, 2023.
Article in Chinese | WPRIM | ID: wpr-995134

ABSTRACT

Pathological insulin resistance (IR) is closely related to gestational diabetes mellitus (GDM) and adverse pregnancy outcomes in women with GDM. Increasing studies have investigated the efficacy of IR indices, such as quantitative insulin sensitivity index, homeostasis model assessment of insulin resistance, triglyceride-glucose index and sex hormone-binding globulin, in predicting GDM and related complications in recent years. This article reviews the research progress in the above topics.

19.
Chinese Journal of Perinatal Medicine ; (12): 482-489, 2023.
Article in Chinese | WPRIM | ID: wpr-995128

ABSTRACT

Objective:To analyze the changing trends in maternal mortality ratios (MMRs) and the main cause-specific MMRs in China from 2010 to 2020, evaluate the association between MMRs and pregnancy healthcare and predict the MMRs for the next five years.Methods:Data on MMRs, the main cause-specific MMRs, and maternal healthcare in China from 2010 to 2020 were collected from China Health Statistical Yearbook. Estimated annual percent changes (EAPCs) were used to analyze the trends in MMRs and the main cause-specific MMRs in China. Average growth rate was used to describe the trend of perinatal healthcare indicators, and spearman rank correlation was used to analyze the correlation between MMRs and perinatal healthcare indicators. GM (1,1) model was established to predict the MMRs for the following five years. Results:(1) From 2010 to 2020, the EAPCs were-5.16%,-6.24%, and-4.28%, respectively, indicating downward trends in MMRs in the whole nation, urban and rural areas ( t=-0.98,-12.42 and-8.96, all P<0.001). (2) From 2010 to 2020, the main cause-specific MMRs in China from obstetric hemorrhage, hypertension during pregnancy, amniotic fluid embolism, and liver disease were all in downward trends ( t=-12.42,-5.44,-3.98 and-3.63, all P<0.001). Except for the MMR from hypertension during pregnancy in urban areas (average growth rate =0.51%), all main cause-specific MMRs in both urban and rural areas decreased significantly, especially the MMRs from hepatopathy in urban and rural areas (average growth rate=-10.40% and-13.96%). (3) The nation wide MMR was negatively correlated with maternal system management rate ( r s=-0.80, P=0.003), prenatal examination rate ( r s=-0.97, P<0.001), postpartum visit rate ( r s=-0.82, P=0.002) and hospital delivery rate ( r s=-0.98, P<0.001). Negative correlations were also found between the MMR and hospital delivery rate in both urban ( r s=-0.82, P=0.002) and rural areas ( r s=-0.95, P<0.001). (4) The GM (1, 1) models for forecasting MMRs in the whole nation, urban and rural areas were established with an accuracy of level 1. The MMR was predicted to show a downward trend in the following five years. The MMRs in China were 15.86/100 000 in 2021 and 15.13/100 000 in 2022 through prediction, similar to the 16.1/100 000 and 15.7/100 000 as announced by the government. Conclusions:The overall MMR in China shows a downward trend, and it dropped faster in urban areas than the rural areas. In addition, it is predicted that the MMR will continue to decline in the following five years, but the gap between urban and rural areas will remain.

20.
Chinese Journal of Anesthesiology ; (12): 692-696, 2023.
Article in Chinese | WPRIM | ID: wpr-994246

ABSTRACT

Objective:To construct a prediction model for difficult tracheal intubation in the patients with obstructive sleep apnea-hypopnea syndrome (OSAHS).Methods:A total of 324 patients with OSAHS undergoing surgery with general anesthesia, admitted to our hospital from June 2019 to June 2021, were included in model group, and 175 patients with OSAHS undergoing surgery with general anesthesia, admitted from July 2021 to July 2022, were selected and served as validation group. The patients in model group were divided into occurrence group and non-occurrence group according to whether difficult tracheal intubation occurred. Logistic regression was used to construct the prediction model, and R4.2.1 software was used to draw the risk nomogram and calibration curve. The predictive accuracy of the model was evaluated by the area under the receiver operating characteristic curve.Results:Body mass index (BMI), sagittal minimum cross-sectional area, horizontal minimum cross-sectional area and mandibular distance were risk factors for difficult tracheal intubation in OSAHS patients ( P<0.05). A prediction model was developed using the above factors: Logit P=33.726+ 1.411×BMI score-0.014×sagittal airway minimum cross-sectional area-0.013×airway horizontal minimum cross-sectional area-0.312× mandibular distance. The area under the receiver operating characteristic curve was 0.846, Youden index 0.585, sensitivity 0.831, specificity 0.755, and the accuracy 0.889 (Hosmer-Lemeshow test χ2=9.24, P=0.322) in model group. The area under the external validation curve was 0.802, Youden index 0.545, sensitivity 0.636, specificity 0.908, and the accuracy 0.893 (Hosmer-Lemeshow test χ2=10.24, P=0.287) in validation group. Conclusions:The prediction model based on BMI, sagittal minimum cross-sectional area of airway, horizontal minimum cross-sectional area of airway and mandibular distance has a high value in predicting the risk of difficult tracheal intubation in patients with OSAHS.

SELECTION OF CITATIONS
SEARCH DETAIL