A Comparitive Study of Clasification Liver Dysfunction with Machine Learning

2018 ◽  
Author(s):  
Sattarpoom Thaiparnit ◽  
Narumol Chumuang ◽  
Mahasak Ketcham
Keyword(s):  
Machine Learning ◽  
Liver Dysfunction

scholarly journals Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0239266
Author(s):  
Junfeng Peng ◽  
Mi Zhou ◽  
Chuan Chen ◽  
Xiaohua Xie ◽  
Ching-Hsing Luo
Keyword(s):  
Machine Learning ◽  
Liver Dysfunction ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Exacerbation Risk

scholarly journals Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhihua Zhong ◽  
Xin Yuan ◽  
Shizhen Liu ◽  
Yuer Yang ◽  
Fanna Liu
Keyword(s):  
Machine Learning ◽  
Septic Shock ◽  
Liver Dysfunction ◽  
Heart Surgery ◽  
Prediction Models ◽  
Open Heart Surgery ◽  
Gradient Boosting ◽  
Test Accuracy ◽  
Open Heart ◽  
Extreme Gradient Boosting

AbstractWe aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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