Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.

Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model.

Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus.

Nutrición en el paciente con pancreatitis aguda grave: guía de manejo clínico / Nutrition in the patient with severe acute pancreatitis: clinical management guideline

Objetivo: proponer una guía de manejo para la nutrición en los pacientes con pancreatitis aguda grave basada en la mejor evidencia disponible. Diseño: revisión sistemática y guía de práctica clínica. Población: pacientes críticos con pancreatitis aguda grave hospitalizados en la unidad de cuidados intensivos. Intervenciones y controles: nutrición entérica Vs. parenteral; dieta hipocalórica Vs. normocalórica; glutamina Vs. sin glutamina; arginina Vs. sin arginina; ácidos grasos omega 3/omega 6 Vs. omega 6 solamente; prebióticos o probióticos Vs. dieta estándar. Desenlaces: mortalidad, estancia hospitalaria y en la unidad de cuidados intensivos y, infecciones y sepsis, disfunción de órganos. Materiales y métodos: búsqueda en bases de datos médicas en línea. Resultados: las 765 referencias iniciales fueron filtradas con las palabras clave y quedaron 69 artículos. Cinco artículos más de autores latinoamericanos o de habla hispana fueron también revisados por considerarlos pertinentes. Conclusiones: los pacientes con pancreatitis aguda grave se benefician del soporte nutricional y la vía entérica es de elección con dietas normocalóricas de inicio temprano, preferencialmente en los primeros tres días. La intervención nutricional pudiera ser un determinante al disminuir la morbilidad y la mortalidad elevada en esta patología pero aún faltan estudios bien diseñados que permitan determinar con claridad el efecto de cada uno de sus componentes.

Objective: to propose a clinical practice guideline of nutrition for patients with severe acute pancreatitis derived from the best available evidence. Design: systematic review and clinical practice guideline.Setting: critically ill patients at the intensive care unit. Patients: with severe acute pancreatitis. Intervention: enteral or parenteral nutrition. Glutamine or no glutamine. Arginine or no arginine. Omega 3 fatty acid or control. Outcome: mortality, pulmonary, catheter associated or pancreatic infection, sepsis, ICU or hospital length of stay.Methodology: medical database online search.Results: 765 initial references were filtered with key words and 69 remained. 5 other articles in Spanish were considered pertinent. Conclusions: patients with severe acute pancreatitis benefit from nutritional support, and isocaloric early enteral route is of choice during the first 3 days. Nutritional intervention could be a determinant in lowering the high morbidity and mortality rates linked with this entity, but well designed trials that elucidate the true individual effect of each constituent are scarce.