Lake water-level fluctuation forecasting using machine learning models: a systematic review

2020 ◽  
Vol 27 (36) ◽  
pp. 44807-44819
Author(s):  
Senlin Zhu ◽  
Hongfang Lu ◽  
Mariusz Ptak ◽  
Jiangyu Dai ◽  
Qingfeng Ji
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Water Level ◽  
Lake Water ◽  
Water Level Fluctuation ◽  
Level Fluctuation ◽  
Learning Models ◽  
Lake Water Level ◽  
Machine Learning Models

Systematic Review of Machine Learning Models for Personalised Dosing of Heparin

2021 ◽  
Author(s):  
Nazanin Falconer ◽  
Ahmad Abdel‐Hafez ◽  
Ian A. Scott ◽  
Sven Marxen ◽  
Stephen Canaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Models ◽  
Machine Learning Models

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

scholarly journals Evaluating Machine Learning Models for Sepsis Prediction: A Systematic Review of Methodologies

iScience ◽  
2021 ◽  
pp. 103651
Author(s):  
Hong-Fei Deng ◽  
Ming-Wei Sun ◽  
Yu Wang ◽  
Jun Zeng ◽  
Ting Yuan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Models ◽  
Machine Learning Models

Effect of Three Gorges Dam on Poyang Lake water level at daily scale based on machine learning

2021 ◽  
Vol 31 (11) ◽  
pp. 1598-1614
Author(s):  
Sheng Huang ◽  
Jun Xia ◽  
Sidong Zeng ◽  
Yueling Wang ◽  
Dunxian She
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Lake Water ◽  
Poyang Lake ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
Lake Water Level ◽  
Daily Scale
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