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14 results on '"Diabetes/diagnosis"'

1. High prevalence of diabetes in a regional Australian hospital highlights the need to prioritise inpatient diabetes care.

Author

Fogarty, Michael, Hung, Annie, Fearnside, Katie, Hazara, Ali, Goh, Amanda, Agarwal, Ankira, Siever, Miriam, Kyi, Mervyn, Barmanray, Rahul, Wang, Ray, Johnson, Douglas, Puri, Gaurav, and Fourlanos, Spiros

Subjects
*TREATMENT of diabetes, *REGIONAL medical programs, *MEDICAL care use, *PATIENTS, *HOSPITAL care, *HOSPITAL admission & discharge, *MEDICAL care, *DISEASE prevalence, *HEALTH planning, *HEALTH facilities, *DIABETES
Abstract

Regional centres have smaller workforces in acute diabetes care compared to their metropolitan counterparts. A cross‐sectional audit performed at Albury Hospital identified a high prevalence (34%) of diabetes for inpatients compared with metropolitan centres. The high prevalence highlights the need for all healthcare services to consider appropriate resources for the management of diabetes in people admitted to hospital. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Modelo de inteligencia artificial para la detección temprana de diabetes.

Author

Hoyos, William, Hoyos, Kenia, and Ruiz-Pérez, Rander

Abstract

Copyright of Biomédica: Revista del Instituto Nacional de Salud is the property of Instituto Nacional de Salud of Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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3. Artificial intelligence model for early detection of diabetes

Author

Hoyos, William, Hoyos, Kenia, Ruiz-Pérez, Rander, Hoyos, William, Hoyos, Kenia, and Ruiz-Pérez, Rander

Abstract

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 decisionmaking 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., 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.

Published
2023

4. Artificial intelligence model for early detection of diabetes

Author

Hoyos W, Hoyos K, and Ruiz-Pérez R

Subjects
Humans, Early Diagnosis, Artificial Intelligence, Diabetes Mellitus diagnosis, Diabetes Mellitus epidemiology
Abstract

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 decisionmaking 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.

Published
2023
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5. Diabetes-related antibody-testing is a valuable screening tool for identifying monogenic diabetes – A survey from the worldwide SWEET registry

Author

Catarina Limbert, Stefanie Lanzinger, Carine deBeaufort, Violeta Iotova, Julie Pelicand, Mariana Prieto, Riccardo Schiaffini, Zdeněk Šumnik, and Danièle Pacaud

Subjects
Glycated Hemoglobin, Adolescent, C-Peptide, Endocrinology, Diabetes and Metabolism, General Medicine, Diabetes Mellitus, Type 1, HDE END PED, Endocrinology, Diabetes/monogenic, Diabetes Mellitus, Internal Medicine, Humans, Mass Screening, Registries, Child, SWEET registry, Diabetes/diagnosis
Abstract

Aims: To evaluate access to screening tools for monogenic diabetes in paediatric diabetes centres across the world and its impact on diagnosis and clinical outcomes of children and youth with genetic forms of diabetes. Methods: 79 centres from the SWEET diabetes registry including 53,207 children with diabetes participated in a survey on accessibility and use of diabetes related antibodies, c-peptide and genetic testing. Results: 73, 63 and 62 participating centres had access to c-peptide, antibody and genetic testing, respectively. Access to antibody testing was associated with higher proportion of patients with rare forms of diabetes identified with monogenic diabetes (54 % versus 17 %, p = 0.01), lower average whole clinic HbA1c (7.7[Q1,Q2: 7.3-8.0]%/61[56-64]mmol/mol versus 9.2[8.6-10.0]%/77[70-86]mmol/mol, p < 0.001) and younger age at onset (8.3 [7.3-8.8] versus 9.7 [8.6-12.7] years p < 0.001). Additional access to c-peptide or genetic testing was not related to differences in age at onset or HbA1c outcome. Conclusions: Clinical suspicion and antibody testing are related to identification of different types of diabetes. Implementing access to comprehensive antibody screening may provide important information for selecting individuals for further genetic evaluation. In addition, worse overall clinical outcomes in centers with limited diagnostic capabilities indicate they may also need support for individualized diabetes management. info:eu-repo/semantics/publishedVersion

Published
2022
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6. Influence of Type 1 Diabetes on the Symbolic Analysis and Complexity of Heart Rate Variability in Young Adults

Author

Diego Giuliano Destro Christofaro, Luiz Carlos Marques Vanderlei, Franciele Marques Vanderlei, Rayana Loch Gomes, Laís Manata Vanzella, Elaine Aparecida de Oliveira, Anne Kastelianne França da Silva, and Universidade Estadual Paulista (Unesp)

Subjects
Male, lcsh:Diseases of the circulatory (Cardiovascular) system, Time Factors, Entropy, Diabetic Neuropathies/prevention, 0206 medical engineering, Primary Dysautonomias, 02 engineering and technology, 030204 cardiovascular system & hematology, Autonomic Nervous System, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Diabetes / therapy, Heart Rate, Diabetes Mellitus, Diabetes Mellitus / complications, Humans, Medicine, Diabetes / diagnosis, Diabetes Mellitus / complicações, Sistema Nervoso Autônomo, Diabetes/therapy, Diabetes / diagnóstico, Diabetes / terapia, business.industry, Diabetes diagnosis, Adulto Jovem, Diabetes mellitus/complications, 020601 biomedical engineering, Cross-Sectional Studies, Diabetes Mellitus, Type 1, Autonomic Nervous System Diseases, Nonlinear Dynamics, lcsh:RC666-701, Case-Control Studies, Frequência Cardíaca, Female, Short Editorial, Cardiology and Cardiovascular Medicine, business, Humanities, Diabetes/diagnosis, Diabetes Mellitus Complications
Abstract

Background: Type 1 diabetes mellitus can cause autonomic changes, which can be assessed by heart rate variability. Among the heart rate variability assessment methods, the symbolic analysis and Shannon entropy, based on the Chaotic dynamics, have gained prominence. Objective: To compare heart rate variability indexes, obtained through symbolic analysis and Shannon entropy, in young adults with type 1 diabetes mellitus and healthy young individuals, associated with the analysis of linear indexes; and to verify if there are associations between the indexes obtained by the symbolic analysis and by Shannon entropy and linear indexes in diabetic individuals. Methods: Heart rate variability data from 39 young adults with type 1 diabetes mellitus and 43 healthy young individuals were analyzed, using a cardio-frequency meter. Linear indexes (standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds; square root of the mean of the squared differences between adjacent normal RR intervals in a time interval expressed in milliseconds; low and high frequency components in millisecond squared; and normalized units and ratio between low and high frequency components) and nonlinear ones (Shannon entropy and symbolic analysis - standard without variation; with one or two variations; and with two different variations) of the heart rate variability were calculated. The statistical significance was set at 5%, and the confidence interval was 95%. Results: Significantly lower values were observed in the DM1 group compared to healthy young adults for the standard deviation indexes of all normal RR intervals recorded in a time interval [37.30 (29.90) vs. 64.50 (36.20); p = 0.0001], square root of the mean of the squared differences between adjacent normal RR intervals in a time interval [32.73 (17.43) vs. 55.59 (21.60); p = 0.0001], low frequency component [402.00 (531.00) vs. 1,203.00 (1,148.00); p = 0.0001], high frequency component [386.00 (583.00) vs. 963.00 (866.00); p = 0.0001] and the pattern with two different variations [15,33 (9,22) vs. 20.24 (12.73); p = 0.0114], with the effect of this difference being considered large (standard deviation of all normal RR intervals recorded in a time interval, square root of the mean of the squared differences between adjacent normal RR intervals in a time interval and low frequency component), medium (high frequency component) and small (standard with two different variations). The agreement of the associations between the linear and non-linear indexes was considered elevated for the high frequency component index - normalized units (r = -0.776), with the standard index without variation, and moderate for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.550), standard deviation of all normal RR intervals recorded in a time interval (r = 0.522), high frequency component - normalized units (r = 0.638) with the index standard with two similar variations, as well as for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.627) and high frequency component - normalized units (r = 0.601) with the index standard with two different variations. Conclusion: Type 1 diabetes mellitus influenced linear indexes and symbolic analysis, but not yet in the complexity of heart rate variability. Additionally, heart rate variability indexes correlated with the symbolic dynamics. Resumo Fundamento: O diabetes melito tipo 1 pode promover alterações autonômicas, que podem ser avaliadas pela variabilidade da frequência cardíaca. Dentre os métodos da variabilidade da frequência cardíaca, têm ganhado destaque a análise simbólica e a entropia de Shannon, baseadas na dinâmica do caos. Objetivo: Comparar índices da variabilidade da frequência cardíaca obtidos por meio da análise simbólica e da entropia de Shannon, entre jovens com diabetes melito tipo 1 e jovens saudáveis, associados à análise de índices lineares; e verificar se há associações entre os índices obtidos pela análise simbólica e pela entropia de Shannon e índices lineares em indivíduos diabéticos. Métodos: Foram analisados dados da variabilidade da frequência cardíaca de 39 jovens com diabetes melito tipo 1 e 43 jovens saudáveis, obtidos por meio de um cardiofrequencímetro. Foram calculados os índices lineares (desvio padrão de todos os intervalos RR normais gravados em um intervalo de tempo expresso em milissegundo; raiz quadrada da média do quadrado das diferenças entre intervalos RR normais adjacentes em um intervalo de tempo expresso em milissegundo; componentes de baixa e alta frequência, em milissegundo ao quadrado; e unidades normalizadas e razão entre componente de baixa e alta frequência) e não lineares (entropia de Shannon e análise simbólica - padrão sem variação; com uma ou duas variações; e com duas variações diferentes) da variabilidade da frequência cardíaca. A significância estatística adotada foi fixada em 5%, e o intervalo de confiança em 95%. Resultados: Foram observados valores significativamente menores no Grupo DM1 em comparação aos jovens saudáveis para os índices desvio padrão de todos os intervalos RR normais gravados em um intervalo de tempo [37,30 (29,90) vs. 64,50 (36,20); p = 0,0001], raiz quadrada da média do quadrado das diferenças entre intervalos RR normais adjacentes em um intervalo de tempo [32,73 (17,43) vs. 55,59 (21,60); p = 0,0001], componente de baixa frequência [402,00 (531,00) vs. 1.203,00 (1.148,00); p = 0,0001], componente de alta frequência [386,00 (583,00) vs. 963,00 (866,00); p = 0,0001] e padrão com duas variações diferentes [15,33 (9,22) vs. 20,24 (12,73); p = 0,0114], sendo o efeito desta diferença considerado grande (desvio padrão de todos os intervalos RR normais gravados em um intervalo de tempo, raiz quadrada da média do quadrado das diferenças entre intervalos RR normais adjacentes em um intervalo de tempo e componente de baixa frequência), médio (componente de alta frequência) e pequeno (padrão com duas variações diferentes). A concordância das associações entre os índices lineares e não lineares foi considerada elevada para o índice componente de alta frequência - unidades normalizadas (r = -0,776), com o índice padrão sem variação, e moderada para os índices raiz quadrada da média do quadrado das diferenças entre intervalos RR normais adjacentes em um intervalo de tempo (r = 0,550), desvio padrão de todos os intervalos RR normais gravados em um intervalo de tempo (r = 0,522), componente de alta frequência - unidades normalizadas (r = 0,638) com o índice padrão com duas variações similares, assim como para os índices raiz quadrada da média do quadrado das diferenças entre intervalos RR normais adjacentes em um intervalo de tempo (r = 0,627) e componente de alta frequência - unidades normalizadas (r = 0,601) com o índice padrão com duas variações diferentes. Conclusão: O diabetes melito tipo 1 influenciou nos índices lineares e na análise simbólica, mas ainda não na complexidade da variabilidade da frequência cardíaca. Além disso, índices de variabilidade da frequência cardíaca apresentaram correlação com a dinâmica simbólica.

Published
2018

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