Titre : Organismes

Organismes : Questions médicales fréquentes

Termes MeSH sélectionnés :

Models, Statistical
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"Question", "name": "Quels outils pour le diagnostic des maladies infectieuses ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Les PCR, cultures et tests rapides sont des outils clés pour le diagnostic." } }, { "@type": "Question", "name": "Comment les épidémies sont-elles surveillées ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Les systèmes de surveillance épidémiologique collectent et analysent des données de santé." } }, { "@type": "Question", "name": "Quels symptômes d'une infection bactérienne ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Fièvre, douleur, fatigue et inflammation sont des symptômes courants d'infections bactériennes." } }, { "@type": "Question", "name": "Comment reconnaître une infection virale ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Les symptômes incluent fièvre, toux, fatigue et douleurs musculaires, souvent sans localisation." } }, { "@type": "Question", "name": "Quels 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"text": "Les vaccins et prophylaxies médicamenteuses aident à prévenir certaines infections." } }, { "@type": "Question", "name": "Quelles complications des infections bactériennes ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Les complications peuvent inclure septicémie, choc et défaillance d'organes." } }, { "@type": "Question", "name": "Quels risques liés aux infections virales ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Les infections virales peuvent entraîner des pneumonies, des encéphalites et des complications chroniques." } }, { "@type": "Question", "name": "Quelles complications des infections fongiques ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Les infections fongiques peuvent causer des infections systémiques graves, surtout chez les immunodéprimés." } }, { "@type": "Question", "name": "Quels effets à long terme des infections parasitaires ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Les infections parasitaires peuvent entraîner des troubles nutritionnels et des complications chroniques." } }, { "@type": "Question", "name": "Comment les infections peuvent-elles affecter la santé mentale ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Les infections graves peuvent entraîner des troubles anxieux et dépressifs chez certains patients." } }, { "@type": "Question", "name": "Quels facteurs augmentent le risque d'infections ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "L'âge, l'immunodépression, le diabète et les conditions de vie précaires augmentent le risque." } }, { "@type": "Question", "name": "Comment l'hygiène influence-t-elle le risque d'infection ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Une mauvaise hygiène personnelle et environnementale augmente le risque d'infections." } }, { "@type": "Question", "name": "Quel rôle joue la vaccination dans la réduction des risques ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "La vaccination réduit le risque d'infections en stimulant la réponse immunitaire." } }, { "@type": "Question", "name": "Comment le stress affecte-t-il le risque d'infection ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Le stress chronique peut affaiblir le système immunitaire, augmentant le risque d'infections." } }, { "@type": "Question", "name": "Quels comportements à risque favorisent les infections ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Le tabagisme, l'alcoolisme et les rapports sexuels non protégés augmentent le risque d'infections." } } ] } ] }

Sources (10000 au total)

Development of predictive statistical shape models for paediatric lower limb bones.

Accurate representation of bone shape is important for subject-specific musculoskeletal models as it may influence modelling of joint kinematics, kinetics, and muscle dynamics. Statistical shape model... We created three-dimensional models of 56 femurs, 29 pelves, 56 tibias, 56 fibulas, and 56 patellae through segmentation of magnetic resonance images taken from 29 typically developing children (15 fe... Femurs, pelves, tibias, fibulas, and patellae reconstructed via SSM using full-input had RMSE between 0.89 ± 0.10 mm (patella) and 1.98 ± 0.38 mm (pelvis), Jaccard indices between 0.77 ± 0.03 (pelvis)... The SSM of paediatric lower limb bones showed reconstruction accuracy consistent with previously developed SSM and outperformed adult-based SSM when used to reconstruct paediatric bones....

The accuracy of statistical shape models in predicting bone shape: A systematic review.

This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.... A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which ... 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).... Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required...

Integrating simulation models and statistical models using causal modelling principles to predict aquatic macroinvertebrate responses to climate change.

Climate change is projected to threaten ecological communities through changes in temperature, rainfall, runoff patterns, and mediated changes in other environmental variables. Their combined effects ...

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.... The study aimed to compare how different model metrics rank the importance of various covariates.... A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available.... This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most ... The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), ... The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models....

Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.

Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. T... In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approach... In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and... While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergen...