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  1. Fulltext A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction
    Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, et al.
    J Environ Manage, 2023 Jan 15;326(Pt B):116813.
    PMID: 36435143 DOI: 10.1016/j.jenvman.2022.116813
    Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.
  2. An international multidisciplinary consensus on pediatric metabolic dysfunction-associated fatty liver disease
    Zhang L, El-Shabrawi M, Baur LA, Byrne CD, Targher G, Kehar M, et al.
    Med, 2024 Jul 12;5(7):797-815.e2.
    PMID: 38677287 DOI: 10.1016/j.medj.2024.03.017
    BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent in children and adolescents, particularly those with obesity. NAFLD is considered a hepatic manifestation of the metabolic syndrome due to its close associations with abdominal obesity, insulin resistance, and atherogenic dyslipidemia. Experts have proposed an alternative terminology, metabolic dysfunction-associated fatty liver disease (MAFLD), to better reflect its pathophysiology. This study aimed to develop consensus statements and recommendations for pediatric MAFLD through collaboration among international experts.

    METHODS: A group of 65 experts from 35 countries and six continents, including pediatricians, hepatologists, and endocrinologists, participated in a consensus development process. The process encompassed various aspects of pediatric MAFLD, including epidemiology, mechanisms, screening, and management.

    FINDINGS: In round 1, we received 65 surveys from 35 countries and analyzed these results, which informed us that 73.3% of respondents agreed with 20 draft statements while 23.8% agreed somewhat. The mean percentage of agreement or somewhat agreement increased to 80.85% and 15.75%, respectively, in round 2. The final statements covered a wide range of topics related to epidemiology, pathophysiology, and strategies for screening and managing pediatric MAFLD.

    CONCLUSIONS: The consensus statements and recommendations developed by an international expert panel serve to optimize clinical outcomes and improve the quality of life for children and adolescents with MAFLD. These findings emphasize the need for standardized approaches in diagnosing and treating pediatric MAFLD.

    FUNDING: This work was funded by the National Natural Science Foundation of China (82070588, 82370577), the National Key R&D Program of China (2023YFA1800801), National High Level Hospital Clinical Research Funding (2022-PUMCH-C-014), the Wuxi Taihu Talent Plan (DJTD202106), and the Medical Key Discipline Program of Wuxi Health Commission (ZDXK2021007).

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