Diagnosis of patients with blood cell count for COVID-19: An explainable artificial intelligence approach / Diagnóstico de pacientes com hemograma para COVID-19: Uma abordagem com inteligência artificial explicável / Diagnóstico de pacientes con hemograma para COVID-19: un enfoque explicable de inteligencia artificial

Objective: Present an explainable artificial intelligence (AI) approach for COVID-19 diagnosis with blood cell count. Methods: Five AI algorithms were evaluated: Logistic Regression, Random Forest, Support Vector Machine, Gradient Boosting and eXtreme Gradient Boosting. A Bayesian optimization with 5-Fold cross-validation was used to hyper-parameters tuning. The model selection evaluated three results: cross validation performance, test set prediction performance and a backtest: performance on identifying patients negative for COVID-19, but positive for others respiratory pathologies. Shapley Additive explanations (SHAP) was used to explain the chosen model. Results: A Random Forest model was obtained with 77.7% F1-Score (IC95%:57.1;92.3), 85.9% AUC (IC95%:73.7;95.9), 74.4% Sensitivity (IC95%:50.0;92.1) and 97.5% Specificity (IC95%:93.6;100.0). The main features were leukocytes, platelets and eosinophils. Conclusion: The research highlights the importance of model interpretability, demonstrating blood cell count as a possibility for COVID-19 diagnosis. The methodological structure developed, using TRIPOD's guidelines, can be extrapolated to other pathologies.

Objetivo: Propor uma abordagem com inteligência artificial explicável para diagnóstico de COVID-19 com hemograma. Métodos: Cinco algoritmos de IA foram testados: Regressão Logística, Florestas Aleatórias, Máquina de Vetores de Suporte, Gradient Boosting e eXtreme Gradient Boosting. Os hiper-parâmetros foram definidos através da otimização bayesiana com validação cruzada 5-Fold. A seleção de modelo utilizou três resultados de desempenho para definir o melhor modelo: validação cruzada, conjunto de teste e rendimento na identificação de pacientes negativos para COVID-19, porém positivos para outras patologias respiratórias (backtest). Ao final, Shapley Additive explanations (SHAP) foi utilizado para explicar o modelo escolhido. Resultados: Obteve-se um modelo Random Forest com F1-Score de 77.7% (IC95%:57.1;92.3), AUC de 85.9% (IC95%:73.7;95.9), Sensibilidade de 74.4% (IC95%:50.0;92.1) e Especificidade de 97.5% (IC95%:93.6;100.0). As principais variáveis foram leucócitos, plaquetas e eosinófilos. Conclusão: A pesquisa destaca a importância da interpretabilidade do modelo, demonstrando o hemograma como uma possibilidade para diagnosticar COVID-19. A estrutura metodológica desenvolvida no estudo, utilizando as diretrizes do TRIPOD, pode ser extrapolada para detecção de outras patologias.

Objetivo: Proponer un enfoque explicable de inteligencia artificial (IA) para el diagnóstico de COVID-19 con el uso de hemograma. Métodos: Cinco modelos de IA fueron evaluados: Logistic Regression, Random Forest, Support Vector Machine, Gradient Boosting e eXtreme Gradient Boosting. Los hiper-parámetros fueron definidos a través de optimización bayesiana con validación cruzada 5-Folds. La selección del modelo se utilizó tres resultados: rendimiento del validación cruzada, rendimento en conjunto de pruebas y el análisis de desempeño en identificación de pacientes negativos para COVID-19, pero positivos para otras patologías respiratorias (backtest). Shapley Additive explanations (SHAP) fue utilizado para explicar el modelo elegido. Resultados: Se obtuvo un modelo Random Forest con F1-Score de 77.7% (IC95%:57.1;92.3), AUC de 85.9% (IC95%:73.7;95.9), Sensibilidad de 74.4% (IC95%:50.0;92.1) y Especificidad de 97.5% (IC95%:93.6;100.0). Las principales variables fueron leucocitos, plaquetas y eosinófilos. Conclusión: La investigación presenta la importancia de la interpretabilidad del modelo, demostrando el uso de hemograma como posibilidad para diagnosticar COVID-19. La estructura elaborada, siguiendo las directrices de TRIPOD, puede ser extrapolar para otras patologías.

Integrating Computational Methods to Investigate the Macroecology of Microbiomes.

Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as "everything is everywhere, but the environment selects") as well as applied ecological problems, such as those posed by human induced global environmental changes.