Prioridades de investigación en salud en colombia: perspectiva de los investigadores

Colombia tiene una escasa experiencia en identificar prioridades de investigación en salud. En el año 2004 se inició un proyecto para identificar prioridades de investigación en salud, entendiendo por tales las que resultan de un ejercicio ordenado de ponderación basado en una valoración juiciosa de problemáticas sanitarias cuya respuesta y/o solución puede lograrse en gran parte por medio de conocimientos y procesos de investigación. Como referentes del proyecto se tuvieron en cuenta algunos de los métodos de priorización utilizados y recomendados en el ámbito internacional, entre ellos la matriz combinada del Global Forum for Health Resarch. Se pusieron en práctica dos trayectos metodológicos principales: por una parte, diseño y aplicación de un método para ponderar u ordenar, de manera cualitativa y cuantitativa, las problemáticas de investigación en salud; por otra parte, construcción de consensos con investigadores y representantes de comunidades científicas. Para identificar las problemáticas de salud predominantes se realizaron dos reuniones nacionales, dos reuniones regionales y un foro virtual. Una vez dentificadas las problemáticas de salud predominantes, con su respectiva estimación de carga de enfermedad, estas se valoraron por políticos y decisores y se calificaron por investigadores de ciencias básicas, ciencias clínicas y salud pública, en términos del aporte del conocimiento requerido para afrontar, controlar o resolver tales problemáticas. Se obtuvieron unas prioridades de investigación en salud por áreas globales: enfermedades crónicas, enfermedades infecciosas emergentes, Tuberculosis/Lepra, infección nosocomial e infecciones de transmisión sexual/VIH/SIDA

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Multiple Correspondence Analysis Syntax for profiling analysis

Multiple Correspondence Analysis Syntax for profiling analysis of the study on Mortality due to traffic accidents in Colombia: Profiles of pedestrians and cyclists, 1998-2019THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Cyclist and pedestrians by population density

Number of cyclist and pedestrians by population densityTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Population densities for the municipalities of Colombia

Population densities calculated for the municipalities of ColombiaTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Pedestrian and cyclist fatalities by Health insurance regime

Pedestrian and cyclist fatalities by Health insurance regimeTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Search strategy for systematic review

Search strategy for systematic review: Benefits in autonomy, functionality, physical performance, physical and mental health status, of low-cost multicomponent physical activity interventions in institutionalized older adults in the community. A systematic reviewTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET

Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model’s performance to that of multiple expert nuclear medicine physicians’ readers. Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer’s disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model’s performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus