Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data.

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanicão' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R2, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors.

Degree-day models for predicting adult Delia platura (Diptera: Anthomyiidae) spring flight and first emergence in New York State.

The seedcorn maggot, Delia platura (Meigen), is a pest affecting many crops, including corn. The early spring emergence of adults and belowground seed damage by maggots leave no room for rescue treatments during the short growing season in New York State. Degree-day (DD) models play a crucial role in predicting insect emergence and adult peak activity and are essential for effective pest management. The current D. platura DD model was launched on the Network for Environment and Weather Applications (NEWA) in 2022, using existing scientific literature from other North American regions. The NEWA model predicted adult D. platura first emergence at an average of 471 (39°F) DD in 2022. To gain an accurate and precise understanding of D. platura adult spring emergence and activity, we used interpolated temperature data to calculate the DD for each specific location where adults were captured in the field. DD calculations were performed using the average method, setting a biofix on January 1st and a base temperature of 39°F. In 2023, overwintering adults emerged at an average of 68 DD, and in 2022, adult activity was registered at an average of 282 DD. Accurately predicting the emergence of D. platura could contribute to informing integrated pest management strategies that incorporate timing and cultural practices over chemical solutions to protect crops and the environment.

Predictive model of suicide risk in Colombian university students: quantitative analysis of associated factors.

Introduction: The risk of suicide and completed suicides among young university students presents critical challenges to mental and public health in Colombia and worldwide. Employing a quantifiable approach to comprehend the factors associated with these challenges can aid in visualizing the path towards anticipating and controlling this phenomenon. Objective: Develop a predictive model for suicidal behavior in university students, utilizing predictive analytics. Method: We conducted an observational, retrospective, cross-sectional, and analytical research study at the University of Manizales, with a focus on predictive applicability. Data from 2,436 undergraduate students were obtained from the research initiative "Building the Future: World Mental Health Surveys International College Students." Results: The top ten predictor variables that generated the highest scores (ranking coefficients) for the sum of factors were as follows: history of sexual abuse (13.21), family history of suicide (11.68), medication (8.39), type of student (7.4), origin other than Manizales (5.86), exposure to cannabis (4.27), exposure to alcohol (4.42), history of physical abuse (3.53), religiosity (2.9), and having someone in the family who makes you feel important (3.09). Discussion: Suicide involves complex factors within psychiatric, medical, and societal contexts. Integrated detection and intervention systems involving individuals, families, and governments are crucial for addressing these factors. Universities also play a role in promoting coping strategies and raising awareness of risks. The predictive accuracy of over 80% in identifying suicide risk underscores its significance. Conclusion: The risk factors related to suicidal behavior align with the findings in specialized literature and research in the field. Identifying variables with higher predictive value enables us to take appropriate actions for detecting cases and designing and implementing prevention strategies.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population.

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Development and validation of a novel scoring system based on a nomogram for predicting inadequate bowel preparation.

BACKGROUND AND AIMS: Adequate bowel preparation (BP) is crucial for the diagnosis of colorectal diseases. Identifying patients at risk of inadequate BP allows for targeted interventions and improved outcomes. We aimed to develop a model for predicting inadequate BP based on preparation-related factors. METHODS: Adult outpatients scheduled for colonoscopy between May 2022 and October 2022 were enrolled. One set (N = 913) was used to develop and internally validate the predictive model. The primary predictive model was displayed as a nomogram and then modified into a novel scoring system, which was externally validated in an independent set (N = 177). Inadequate BP was defined as a Boston Bowel Preparedness Scale (BBPS) score of less than 2 for any colonic segment. The model was evaluated by the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: Independent factors included in the prediction model were stool frequency ≤ 5 (15 points), preparation-to-colonoscopy interval ≥ 5 h (15 points), incomplete dosage (100 points), non-split dose (90 points), unrestricted diet (88 points), no additional water intake (15 points), and last stool appearance as an opaque liquid (0-80 points). The training set exhibited the following performance metrics for identifying BP failure: area under the curve (AUC) of 0.818, accuracy (ACC) of 0.818, positive likelihood ratio (PLR) of 2.397, negative likelihood ratio (NLR) of 0.162, positive predictive value (PPV) of 0.850, and negative predictive value (NPV) of 0.723. In the internal validation set, these metrics were 0.747, 0.776, 2.099, 0.278, 0.866, and 0.538, respectively. The external validation set showed values of 0.728, 0.757, 2.10, 0.247, 0.782, and 0.704, respectively, indicating strong discriminative ability. Calibration curves demonstrated close agreement, and DCA indicated superior clinical benefits at a threshold probability of 0.73 in the training cohort and 0.75 in the validation cohort for this model. CONCLUSIONS: This novel scoring system was developed from a prospective study and externally validated in an independent set based on 7 easily accessible variables, demonstrating robust performance in predicting inadequate BP.

Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology.

Wastewater Treatment Plants (WWTPs) present complex biochemical processes of high variability and difficult prediction. This study presents an innovative approach using Machine Learning (ML) models to predict wastewater quality parameters. In particular, the models are applied to datasets from both a simulated wastewater treatment plant (WWTP), using DHI WEST software (WEST WWTP), and a real-world WWTP database from Santa Catarina Brewery AMBEV, located in Lages/SC - Brazil (AMBEV WWTP). A distinctive aspect is the evaluation of predictive performance in continuous data scenarios and the impact of changes in WWTP operations on predictive model performance, including changes in plant layout. For both plants, three different scenarios were addressed, and the quality of predictions by random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) models were evaluated. The prediction quality by the MLP model reached an R2 of 0.72 for TN prediction in the WEST WWTP output, and the RF model better adapted to the real data of the AMBEV WWTP, despite the significant discrepancy observed between the real and the predicted data. Techniques such as Partial Dependence Plots (PDP) and Permutation Importance (PI) were used to assess the importance of features, particularly in the simulated WEST tool scenario, showing a strong correlation of prediction results with influent parameters related to nitrogen content. The results of this study highlight the importance of collecting and storing high-quality data and the need for information on changes in WWTP operation for predictive model performance. These contributions advance the understanding of predictive modeling for wastewater quality and provide valuable insights for future practice in wastewater treatment.

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage

Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.

Diagnostic value of serum inflammatory markers in predicting early refractoriness of transarterial chemoembolization in patients with Barcelona Clinic Liver Cancer Stage 0, A, and B hepatocellular carcinoma

Transarterial chemoembolization (TACE) is an established therapeutic strategy for intermediate stage Barcelona Clinic Liver Cancer (BCLC) hepatocellular carcinoma (HCC). However, patients who are early refractory to TACE may not benefit from repeated TACE treatment. Our primary objective was to assess the diagnostic value of inflammatory markers in identifying early TACE refractory for patients with early (BCLC 0 and A) or intermediate (BCLC B) stage HCC. We retrospectively reviewed the HCC patients who underwent TACE as the initial treatment in two hospitals. Patients with early TACE refractoriness had significantly poorer median overall survival (OS) (16 vs 40 months, P<0.001) and progression-free survival (PFS) (7 vs 23 months, P<0.001) compared to TACE non-refractory patients. In the multivariate regression analysis, tumor size (P<0.001), bilobular invasion (P=0.007), high aspartate aminotransferase-to-platelet ratio index (APRI) (P=0.007), and high alpha fetoprotein (AFP) level (P=0.035) were independent risk factors for early TACE refractoriness. The predictive model showcasing these factors exhibited high ability proficiency, with an area under curve (AUC) of 0.833 (95%CI=0.774-0.892) in the training cohort, 0.750 (95%CI: 0.640-0.861) in the internal-validation cohort, and 0.733 (95%CI: 0.594-0.872) in the external-validation cohort. Calibration curve analysis revealed good agreement between the actual and predicted probabilities of early TACE refractoriness. Our preliminary study estimated the potential value of inflammatory markers in predicting early TACE refractoriness and provides a predictive model to assist in identifying patients who may not benefit from repeat TACE treatment.

Development and validation of a noninvasive prediction model for significant hepatic liver fibrosis in Chinese patients with autoimmune hepatitis.

INTRODUCTION AND OBJECTIVES: Autoimmune hepatitis (AIH) is a prevalent noninfectious liver disease. However, there is currently a lack of noninvasive tests appropriate for evaluating liver fibrosis in AIH patients. The objective of this study was to develop and validate a predictive model for noninvasive assessment of significant liver fibrosis (S ≥ 2) in patients to provide a reliable method for evaluating liver fibrosis in individuals with AIH. MATERIALS AND METHODS: The clinical data of 374 AIH patients were analyzed. A prediction model was established through logistic regression in the training set, and bootstrap method was used to validate the models internally. In addition, the clinical data of 109 AIH patients were collected for external verification of the model.The model was expressed as a nomogram, and area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model. RESULTS: Logistic regression analysis revealed that age, platelet count (PLT), and the A/G ratio were identified as independent risk factors for liver fibrosis in AIH patients (P < 0.05). The diagnostic model that was composed of age, PLT and A/G was superior to APRI and FIB-4 in both the internal validation (0.872, 95%CI: 0.819-0.924) and external validation (0.829, 95%CI: 0.753-0.904). CONCLUSIONS: Our predictive model can predict significant liver fibrosis in AIH patients more accurately, simply, and noninvasively.

Modelo predictivo de crisis vasoclusiva en la anemia drepanocítica / Predictive model of vasooclusive crisis in the sickle cell deseas

Introducción: El diagnóstico precoz de la crisis vasoclusiva (CVO) que afecta a pacientes con drepanocitosis resulta un tema no resuelto en la actualidad. No se ha encontrado en la literatura evidencia de modelos que puedan establecer tempranamente índices de riesgo de la CVO para la toma de una conducta terapéutica oportuna en estos pacientes. Objetivo: Establecer índices de riesgo en pacientes con drepanocitosis, a partir de la formulación de un modelo predictivo del estado vasoclusivo. Métodos: A partir de un estudio analí tico transversal de casos y controles, realizado en el Centro Hematológico de Santiago de Cuba, se formuló a través de un análisis discriminante, un modelo predictivo del estado de CVO. Se usaron estadígrafos de dispersión (media y desviación estándar) para el establecimiento de índices de riesgo sustentados en él. Resultados: Se formuló un modelo predictivo del estado de CVO que incluyó biomarcadores del estado redox como predictores significativos en el paciente con drepanocitosis. El modelo sustentó los índices de riesgo, estratificados en 3 categorías (riesgo menor, moderado y mayor) que fueron asignados a los pacientes y posibilitó su adecuada clasificación. Conclusiones: El diseño de un modelo predictivo de CVO y el establecimiento de índices de riesgo en pacientes con drepanocitosis permitió una mejor evaluación. La nueva herramienta diagnóstica que se propone resultaría de gran utilidad en los servicios de Hematología, al facilitar una mejor valoración del estado del paciente con drepanocitosis y un tratamiento profiláctico oportuno que minimice las complicaciones asociadas a este estado.

Introduction: The early diagnosis of vasooclusive crisis affecting sickle cell patients is currently an unresolved issue. In the reviewed literature no models have been found able to establish early risk indices of vasooclusive crisis for taking a timely therapeutic behavior in these patients. Objective: To establish risk indices in sickle cell patients based on the formulation of a predictive model of vasoocclusive state. Methods: Based on a cross-sectional case-control analytic study conducted at the Hematological Center of Santiago de Cuba, a predictive model of VOC status was formulated through a discriminant analysis. Dispersion statistics (mean and standard deviation) were used to establish risk indices based on it. Results: A predictive model of the state of VOC that included biomarkers of the redox state as significant predictors of it in sickle cell patients was formulated. The model supported the risk indices, stratified into 3 categories (lower, moderate and higher risk) that were assigned to the patients and allowed an adequate classification of them. Conclusions: The design of a predictive model of VOC and the establishment of risk indices in sickle cell patients allowed a better evaluation of them. The new diagnostic tool proposed in the study would be very useful in the Hematology services, by facilitating a better assessment of the sickle cell patient's condition and a timely prophylactic treatment that minimizes the complications associated with this state.

Modelo predictivo del fracaso de la terapia eléctrica en la fibrilación auricular paroxística / Predictive model of electrical therapy failure in paroxysmal atrial fibrillation

Introducción: La fibrilación auricular es la arritmia recurrente más habitual en la práctica clínica. Su prevalencia se multiplica en la población actual y tiene diferentes causas fisiopatológicas que la convierten en una pandemia mundial. Objetivos: Diseñar un modelo predictivo de fracaso de la terapia eléctrica en pacientes con fibrilación auricular paroxística. Métodos: Se realizó un estudio de casos y controles, con 33 casos y 66 controles. Variables predictoras: edad, fracción de eyección ≤ 40 por ciento, volumen de aurícula izquierda ≥ 34 mL/m2. A partir de la regresión logística se obtuvo un modelo en el que fueron incluidos el valor predictivo positivo, valor predictivo negativo, la sensibilidad y especificidad. Resultados: Los factores de riesgo predictores fueron: edad ≥ 55 años (p= 0,013; odds ratio (OR)= 3,58; intervalo de confianza -IC- 95 por ciento: 1,33-9,67); la fracción de eyección del ventrículo izquierdo (FEVI) ≤ 40 por ciento se observó en 20 pacientes (22,7 por ciento) (p= 0,004; OR= 4,45; IC95 por ciento: 1,54-12,8); presión de aurícula izquierda elevada, volumen de aurícula izquierda elevado (p= 0,004; OR= 3,11; IC95 por ciento: 1,24-8,77), según el modelo de regresión logística. Se realizó la validación interna por división de datos; se confirmó que el modelo pronostica bien los que van a tener éxito en el resultado terapéutico. Conclusiones: El modelo predictivo elaborado está compuesto por los predictores edad > 55 años, FEVI; volumen de aurícula izquierda; presenta un buen ajuste y poder discriminante, sobre todo valor predictivo positivo(AU)

Introduction: Atrial fibrillation is the most common recurrent arrhythmia in clinical practice. Its prevalence is multiplying in the current population and has different pathophysiological causes that make it a global pandemic. Objectives: To design a predictive model for failure of electrical therapy in patients with paroxysmal atrial fibrillation. Methods: A case-control study was carried out with 33 cases, and 66 controls. Predictor variables: age, ejection fraction ≤ 40 percent, left atrial volume ≥ 34 mL/m2. From logistic regression, a model was obtained in which the positive predictive value, negative predictive value, sensitivity and specificity were included. Results: The predictive risk factors were: age ≥ 55 years (p= 0.013; odds ratio (OR)= 3.58; 95 percent confidence interval -CI-: 1.33-9.67); left ventricular ejection fraction (LVEF) ≤ 40 percent was observed in 20 patients (22.7 percent) (p= 0.004; OR= 4.45; 95 percent CI: 1.54-12.8); elevated left atrial pressure, elevated left atrial volume (p= 0.004; OR= 3.11; 95 percent CI: 1.24-8.77), according to the logistic regression model. Internal validation was carried out by data division; It was confirmed that the model predicts very well those who will be successful in the therapeutic result. Conclusions: The predictive model developed is composed of the predictors age > 55 years, LVEF; left atrial volume; It presents a good fit and discriminating power, especially positive predictive value(AU)

Case studies of clinical decision-making through prescriptive models based on machine learning.

BACKGROUND: The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities. MATERIALS AND METHODS: In this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases. RESULTS: The performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections. CONCLUSIONS: The developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation.

Prognostic predictive model for PONV in cesarean delivery

Introduction: Apfel simplified risk score for postoperative nausea and vomiting (PONV) has shown to be useful in anesthesia; however, since it has not been calibrated in regional anesthesia or in pregnant patients, its use in cesarean section is limited. Objective: To develop a prognostic predictive model for postoperative nausea and vomiting in pregnant patients undergoing cesarean section under spinal anesthesia. Methods: In a cohort of 703 term pregnant patients scheduled of cesarean section, 15 variables were prospectively assessed, to design a prognostic predictive model for the development of postoperative nausea and vomiting. A logistic regression analysis was used to construct the model and its calibration and discrimination were based on the Hosmer-Lemeshow test, the calibration curves, and C statistic. Additionally, the internal calibration was performed with the Bootstrap resampling method. Results: Postoperative nausea and vomiting were experienced by 27% of the patients during the first six hours after surgery. The model included as prognostic variables the development of intraoperative nausea and vomiting, age under 28 years, a history of PONV, the mother's BMI and the weight of the newborn baby. The model showed an adequate calibration (x2: 4.65 p: 0.5888), though a low discrimination (Statistic C = 0.68). Conclusions: A prognostic predictive model was created for the development of PONV in cesarean section. This model was used to build a prognostic scale for the classification of patients into risk groups.

Introducción: La escala de riesgo simplificada de Apfel para náuseas y vómito posoperatorio (NVPO) ha mostrado utilidad en anestesia; sin embargo, al no haber sido calibrada en anestesia regional o en pacientes embarazadas, su utilidad en cesárea es limitado. Objetivo: Desarrollar un modelo de predicción pronóstica para náuseas y vómito posoperatorios en pacientes embarazadas, llevadas a cesárea bajo anestesia espinal. Métodos: En una cohorte de 703 pacientes con embarazo a término programadas para cesárea, se evaluaron 15 variables de forma prospectiva para construir un modelo de predicción pronóstica para el desarrollo de náuseas y vómito posoperatorio. Se utilizó el análisis de regresión logística para la construcción del modelo y se calculó su calibración y discriminación con la prueba de Hosmer-Lemeshow, las curvas de calibración y el estadístico C. Además, se realizó la calibración interna con el método de remuestreo Bootstrap. Resultados: Las náuseas y vómito posoperatorio se presentaron en el 27% de las pacientes durante las primeras seis horas después de la cirugía. El modelo incluyó como variables pro-nósticas el desarrollo de náuseas y vómito en el intraoperatorio, edad menor de 28 años, antecedentes de NVPO, índice de masa corporal (IMC) de la madre y el peso del recién nacido. El modelo mostró una adecuada calibración (x2: 4,65 p: 0,5888), aunque una baja discriminación (Estadístico C = 0,68). Conclusiones: Se construyó un modelo de predicción pronóstica para el desarrollo de NVPO en cirugía cesárea, y con este se construyó una escala pronóstica que permite clasificar a las pacientes por grupos de riesgo.

Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud / A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care

Introducción: En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad. Objetivo: Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial. Métodos: La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas. Resultados: El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 por ciento un área bajo la curva ROC de 0,987 en la validación cruzada. Conclusiones: Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud(AU)

Introduction: In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality. Objective: To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques. Methods: The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied. Results: The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2percent and an area under the ROC curve of 0.987 in the cross validation. Conclusions: A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care(AU)

Aprendizaje automático aplicado a la predicción de diabetes mellitus, utilizando información socioeconómica y ambiental de usuarios del sistema de salud

Objetivo: Se propuso aplicar modelos basados en técnicas de aprendizaje automático como apoyo para el diagnóstico temprano de la diabetes mellitus, utilizando variables de datos ambientales, sociales, económicos y sanitarios, sin la dependencia de la toma de muestras clínicas. Metodología: Se utilizaron datos de 10 889 usuarios afiliados al régimen subsidiado de salud de la zona suroccidental en Colombia, diagnosticados con hipertensión y agrupados en usuarios sin (74,3 %) y con (25,7 %) diabetes mellitus. Se entrenaron modelos supervisados utilizando k vecinos más cercanos, árboles de decisión y bosques aleatorios, así como modelos basados en ensambles, aplicados a la base de datos antes y después de balancear el número de casos en cada grupo de diagnóstico. Se evalúo el rendimiento de los algoritmos mediante la división de la base de datos en datos de entreno y de prueba (70/30, respectivamente), y se utilizaron métricas de exactitud, sensibilidad, especificidad y área bajo la curva. Resultados: Los valores de sensibilidad aumentaron considerablemente al utilizar datos balanceados, pasando de valores máximos del 17,1 % (datos sin balancear) a valores de hasta 57,4 % (datos balanceados). El valor más alto de área bajo la curva (0,61) fue obtenido con los modelos de ensambles, al aplicar un balance en el número de datos por cada grupo y al codificar las variables categóricas. Las variables de mayor peso estuvieron asociadas con aspectos hereditarios (24,65 %) y con el grupo étnico (5.59 %), además de la dificultad visual, el bajo consumo de agua, una dieta baja en frutas y verduras, y el consumo de sal y azúcar. Conclusiones: Aunque los modelos predictivos, utilizando información socioeconómica y ambiental de las personas, surgen como una herramienta para el diagnóstico temprano de la diabetes mellitus, estos aún deben ser mejorados en su capacidad predictiva.

Objective: The objective was to apply models based on machine learning techniques to support the early diagnosis of diabetes mellitus, using environmental, social, economic and health data variables, without dependence on clinical sample collection. Methodology: Data from 10,889 users affiliated with the subsidized health system in the southwestern area of Colombia, diagnosed with hypertension and grouped into users without (74.3%) and with (25.7%) diabetes mellitus, were used. Supervised models were trained using k-nearest neighbors, decision trees, and random forests, as well as ensemble-based models, applied to the database before and after balancing the number of cases in each diagnostic group. The performance of the algorithms was evaluated by dividing the database into training and test data (70/30, respectively), and metrics of accuracy, sensitivity, specificity, and area under the curve were used. Results: Sensitivity values increased significantly when using balanced data, going from maximum values of 17.1% (unbalanced data) to values as high as 57.4% (balanced data). The highest value of area under the curve (0.61) was obtained with the ensemble models, by applying a balance in the amount of data for each group and by coding the categorical variables. The variables with the greatest weight were associated with hereditary aspects (24.65%) and with the ethnic group (5.59%), in addition to visual difficulty, low water consumption, a diet low in fruits and vegetables, and the consumption of salt and sugar. Conclusions: Although predictive models, using people's socioeconomic and environmental information, emerge as a tool for the early diagnosis of diabetes mellitus, their predictive capacity still needs to be improved.

Objetivo: Propôs-se aplicar modelos baseados em técnicas de aprendizagem automática como apoio para o diagnóstico precoce da diabetes mellitus, utilizando variáveis de dados ambientais, sociais, econômicos e sanitários, sem a dependência da coleta de amostras clínicas. Metodologia: Usaram-se dados de 10.889 usuários filiados ao regime subsidiado de saúde da zona sudoeste da Colômbia, diagnosticados com hipertensão e agrupados em usuários sem (74,3%) e com (25,7%) diabetes mellitus. Foram treinados modelos supervisionados utilizando k vizinhos mais próximos, árvores de decisão e florestas aleatórias, assim como modelos baseados em montagens, aplicados à base de dados antes de depois de equilibrar o número de casos em cada grupo de diagnóstico. Avaliou-se o desempenho dos algoritmos por meio da divisão da base de dados de treino e teste (70/30, respectivamente), e utilizaram-se métricas de exatidão, sensibilidade, especificidade e área sob a curva. Resultados: Os valores de sensibilidade aumentaram de maneira significativa ao utilizar dados equilibrados, passando de valores máximos de 17,1% (dados sem equilibrar) a valores de até 57,4% (dados equilibrados). O valor mais elevado de área sob a curva (0,61) foi obtido com os modelos de montagens, ao aplicar um balanço no número de dados por cada grupo e codificar as variáveis categóricas. As variáveis de maior peso estiveram associadas com fatores hereditários (24,65%) e com o grupo étnico (5,59%), além da dificuldade visual, o baixo consumo de água, um regime baixo em frutas e vegetais e o consumo de sal e açúcar. Conclusões: Embora os modelos preditivos, utilizando informação socioeconômica e ambiental das pessoas, surgem como uma ferramenta para o diagnóstico precoce da diabetes mellitus, ainda devem ser melhorados em sua capacidade preditiva.

Modelo predictivo del riesgo de amputación en pacientes con pie diabético / Predictive Model of Amputation Risk in Patients with Diabetic Foot

Introducción: El pie diabético es una complicación de la diabetes mellitus y cada 20 segundos se realiza una amputación ocasionada por esta causa. La estratificación adecuada del paciente con pie diabético permite realizar acciones oportunas para evitar, en un porcentaje elevado de casos, las amputaciones. Objetivo: Validar un modelo predictivo para el riesgo de amputación en pacientes con pie diabético. Métodos: Estudio longitudinal prospectivo. La muestra estuvo conformada por 280 pacientes divididos en dos grupos. Las variables utilizadas para la confección del modelo fueron: edad, hematocrito, leucocitos, linfocitos, neutrófilos, albumina, índice neutrófilo/linfocito, plaquetas, creatinina, tamaño de la lesión y grado de la lesión. Se aplicó un modelo predictivo cuya puntuación permitió estratificar de forma individual a cada paciente. Resultados: Se realizó una validación interna con el 60 por ciento de la muestra, que mostró buena capacidad discriminatoria para el 96,0 por ciento con una sensibilidad de 57,69 por ciento, un valor predictivo positivo del 95,4 por ciento y un Alpha de Cronbach de 0,71. En la prueba final y depuración del modelo se trabajó con el 40 por ciento de la muestra y se realizó la validación externa mediante el estadígrafo Alpha de Cronbach = 0,85. El grupo de prueba tuvo una especificidad de 97,40 por ciento y un valor predictivo positivo del 96,65, lo que fue superior al grupo de entrenamiento. Conclusiones: El modelo predictivo mostró ser una herramienta útil para la estratificación del paciente con pie diabético y permitió aplicar una conducta adecuada en cada paciente(AU)

Introduction: The diabetic foot is a complication of diabetes mellitus and every 20 seconds an amputation by this cause is performed. The adequate stratification of the patient with diabetic foot allows timely actions to avoid, in a high percentage of cases, amputations. Objective: To validate a predictive model for amputation risk in patients with diabetic foot. Methods: Prospective longitudinal study. The sample consisted of 280 patients divided into two groups. The variables used to prepare the model were: age, hematocrit, leukocytes, lymphocytes, neutrophils, albumin, neutrophil/lymphocyte index, platelets, creatinine, lesion size and lesion grade. A predictive model was applied whose score allowed each patient to be individually stratified. Results: An internal validation was performed with 60 percent of the sample, which showed good discriminatory capacity for 96.0 percent with a sensitivity of 57.69 percent, a positive predictive value of 95.4 percent and a Cronbach's Alpha of 0.71. In the final test and debugging of the model, 40 percent of the sample was worked with and external validation was carried out using Cronbach's Alpha statistic = 0.85. The test group had a specificity of 97.40 percent and a positive predictive value of 96.65, which was higher than the training group. Conclusions: The predictive model proved to be a useful tool for the stratification of patients with diabetic foot and allowed to apply an appropriate behavior in each patient(AU)

Applicability of the acute leukemia (AL) - EBMT score as a prognostic model for allogeneic hematopoietic stem cell transplantation: a single-center, prospective, cohort study at a reference transplant center in Brazil

Abstract Introduction The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. Objective To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. Methods In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. Results A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p= 0.007), 2.1 (p= 0.009) and 2.47 (p= 0.01) for the 2-year OS, LFS and TRM, respectively. Conclusion This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.

Influence of Climatic Variables on Incidence of Whitefly-Transmitted Begomovirus in Soybean and Bean Crops in North-Western Argentina.

Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluate the relationship between the incidence of begomovirus and weather before and during the crop cycle. Soybean and bean fields from north-western (NW) Argentina were monitored between 2001 and 2018 and classified as moderate (≤50%) or severe (>50%) according to the begomovirus incidence. Bean golden mosaic virus (BGMV) and soybean blistering mosaic virus (SbBMV) were the predominant begomovirus in bean and soybean crops, respectively. Nearly 200 bio-meteorological variables were constructed by summarizing climatic variables in 10-day periods from July to November of each crop year. The studied variables included temperature, precipitation, relative humidity, wind (speed and direction), pressure, cloudiness, and visibility. For bean, high maximum winter temperatures, low spring humidity, and precipitation 10 days before planting correlated with severe incidence. In soybeans, high temperatures in late winter and in the pre-sowing period, and low spring precipitations were found to be good predictors of high incidence of begomovirus. The results suggest that temperature and pre-sowing precipitations can be used to predict the incidence status [predictive accuracy: 80% (bean) and 75% (soybean)]. Thus, these variables can be incorporated in early warning systems for crop management decision-making to reduce the virus impact on bean and soybean crops.

Development and validation of nonattendance predictive models for scheduled adult outpatient appointments in different medical specialties.

INTRODUCTION: Nonattendance is a critical problem that affects health care worldwide. Our aim was to build and validate predictive models of nonattendance in all outpatients appointments, general practitioners, and clinical and surgical specialties. METHODS: A cohort study of adult patients, who had scheduled outpatient appointments for General Practitioners, Clinical and Surgical specialties, was conducted between January 2015 and December 2016, at the Italian Hospital of Buenos Aires. We evaluated potential predictors grouped in baseline patient characteristics, characteristics of the appointment scheduling process, patient history, characteristics of the appointment, and comorbidities. Patients were divided between those who attended their appointments, and those who did not. We generated predictive models for nonattendance for all appointments and the three subgroups. RESULTS: Of 2,526,549 appointments included, 703,449 were missed (27.8%). The predictive model for all appointments contains 30 variables, with an area under the ROC (AUROC) curve of 0.71, calibration-in-the-large (CITL) of 0.046, and calibration slope of 1.03 in the validation cohort. For General Practitioners the model has 28 variables (AUROC of 0.72, CITL of 0.053, and calibration slope of 1.01). For clinical subspecialties, the model has 23 variables (AUROC of 0.71, CITL of 0.039, and calibration slope of 1), and for surgical specialties, the model has 22 variables (AUROC of 0.70, CITL of 0.023, and calibration slope of 1.01). CONCLUSION: We build robust predictive models of nonattendance with adequate precision and calibration for each of the subgroups.

Applicability of the acute leukemia (AL) - EBMT score as a prognostic model for allogeneic hematopoietic stem cell transplantation: a single-center, prospective, cohort study at a reference transplant center in Brazil.

INTRODUCTION: The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. OBJECTIVE: To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. METHODS: In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. RESULTS: A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p = 0.007), 2.1 (p = 0.009) and 2.47 (p = 0.01) for the 2-year OS, LFS and TRM, respectively. CONCLUSION: This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.