A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study

[1]  S. Qin,et al.  Machine learning classifiers for screening nonalcoholic fatty liver disease in general adults , 2023, Scientific Reports.

[2]  Xiaoping Wu,et al.  Development and application of a novel model to predict the risk of non-alcoholic fatty liver disease among lean pre-diabetics with normal blood lipid levels , 2022, Lipids in Health and Disease.

[3]  K. Abeysekera,et al.  Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis , 2022, BMC Gastroenterology.

[4]  K. Camphausen,et al.  Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets , 2022, Cancers.

[5]  H. Sørensen,et al.  Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients , 2022, Comput. Biol. Medicine.

[6]  A. Mainous,et al.  Body composition among adults at a healthy body mass index and association with undetected non-alcoholic fatty liver , 2022, International Journal of Obesity.

[7]  W. Ji,et al.  A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population , 2022, Frontiers in Public Health.

[8]  M. Noureddin,et al.  Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning , 2022, Hepatology communications.

[9]  Anahita Mansoori,et al.  The association between Dietary Diversity Score and odds of nonalcoholic fatty liver disease: a case-control study , 2022, European journal of gastroenterology & hepatology.

[10]  A. Sartorio,et al.  The Role of Aspartate Transaminase to Platelet Ratio Index (APRI) for the Prediction of Non-Alcoholic Fatty Liver Disease (NAFLD) in Severely Obese Children and Adolescents , 2022, Metabolites.

[11]  Wei Xiang Lim,et al.  The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review , 2022, Medical & Biological Engineering & Computing.

[12]  P. Portincasa,et al.  The advantages of physical exercise as a preventive strategy against NAFLD in postmenopausal women , 2021, European journal of clinical investigation.

[13]  L. Henry,et al.  Global burden of NAFLD and chronic liver disease among adolescents and young adults , 2021, Hepatology.

[14]  K. Promrat,et al.  Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database , 2021, World journal of hepatology.

[15]  Yu-Jin Kwon,et al.  Dairy protein intake is inversely related to development of non-alcoholic fatty liver disease. , 2021, Clinical nutrition.

[16]  Jianfei Zheng,et al.  Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis , 2021, BioData Min..

[17]  Shu-sen Zheng,et al.  Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study. , 2021, Hepatobiliary & pancreatic diseases international : HBPD INT.

[18]  Ilaria Gandin,et al.  Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to Intensive care unit , 2021, J. Biomed. Informatics.

[19]  Wei Li,et al.  Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database , 2021, Frontiers in Medicine.

[20]  N. Shomron,et al.  Machine learning-based prediction of COVID-19 diagnosis based on symptoms , 2021, npj Digital Medicine.

[21]  Jiao Wang,et al.  Development and validation of a nomogram for predicting nonalcoholic fatty liver disease in the non-obese Chinese population. , 2020, American journal of translational research.

[22]  N. Jacobson,et al.  Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence , 2020, Scientific Reports.

[23]  L. Hodson,et al.  Non-alcoholic fatty liver disease in adults: Current concepts in etiology, outcomes and management. , 2019, Endocrine reviews.

[24]  Xuefeng Ma,et al.  Proportion of NAFLD patients with normal ALT value in overall NAFLD patients: a systematic review and meta-analysis , 2019, BMC gastroenterology.

[25]  V. Wong,et al.  Prevalence, incidence, and outcome of non-alcoholic fatty liver disease in Asia, 1999-2019: a systematic review and meta-analysis. , 2019, The lancet. Gastroenterology & hepatology.

[26]  Diego Klabjan,et al.  Predicting ICU readmission using grouped physiological and medication trends , 2019, Artif. Intell. Medicine.

[27]  Alex John London,et al.  Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.

[28]  A Gasparrini,et al.  A methodological framework for model selection in interrupted time series studies. , 2018, Journal of clinical epidemiology.

[29]  Q. Ma,et al.  Joint associations of serum uric acid and ALT with NAFLD in elderly men and women: a Chinese cross-sectional study , 2018, Journal of Translational Medicine.

[30]  Shamim Nemati,et al.  An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU , 2017, Critical care medicine.

[31]  S. Kanchanasuwan,et al.  Clinical risk scoring for predicting non‐alcoholic fatty liver disease in metabolic syndrome patients (NAFLD‐MS score) , 2017, Liver international : official journal of the International Association for the Study of the Liver.

[32]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[33]  C. Fox,et al.  Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis. , 2016, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[34]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[35]  Byung-Hoon Lee,et al.  Uric acid induces fat accumulation via generation of endoplasmic reticulum stress and SREBP-1c activation in hepatocytes , 2014, Laboratory Investigation.

[36]  Youming Li,et al.  Association of serum uric acid level with non-alcoholic fatty liver disease: a cross-sectional study. , 2009, Journal of hepatology.

[37]  W. Kim,et al.  Serum activity of alanine aminotransferase (ALT) as an indicator of health and disease , 2008, Hepatology.

[38]  A. Lonardo,et al.  Fasting insulin and uric acid levels but not indices of iron metabolism are independent predictors of non-alcoholic fatty liver disease. A case-control study. , 2002, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[39]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[40]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[41]  K. Goel,et al.  The concept of normal weight obesity. , 2014, Progress in cardiovascular diseases.

[42]  N. Patel,et al.  Nonalcoholic steatohepatitis (NASH) with diabetes: predictors of liver fibrosis. , 2006, Annals of hepatology.

[43]  John C. Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .