Nonalcoholic fatty liver disease (NAFLD) is a medical condition through which more than 5% of fat builds up in the liver. The progressive form of NAFLD results in an irretrievable condition; NASH, which is accompanied by inflammation, ballooning, and fibrosis of the liver cells. NASH can be terminated in hepatocellular carcinoma and cirrhosis. Besides, as one of the most important risk factors is diabetes, the analysis of the prognosis of NAFLD and NASH is a ubiquitous field of study among researchers.
The purpose of this study is to encompass a new vision of prediction of NASH prognosis based on the flow of patient’s lab, physical tests, and demographic data. A machine learning method will contemplate both medical and mathematical outlooks about this disorder.
Results of this predictive analysis will help to use preventive protocols for NAFLD disease before preceding NASH, which might also meet economic profits for healthcare organizations.
Saeed is a Ph.D. candidate in applied science at Simon Fraser University (SFU) with years of experience as a data scientist. After accomplishing his medical degree (MD), he completed a master's degree in biomechanics to commence his professional career in engineering, earning another Master of Biomedical Engineering at UBC, Vancouver. His interest in machine learning and data science led him to successfully conduct multiple projects in recent years, ranging from managing data in healthcare and finance. Saeed is keen on using predictive analysis, causal inference ML and precision medicine. He applied new schemes of the machine learning method and Graph Neural Network (GNN) to deal with the problem of discrete and irregular time series in clinical data. In 2021 he started a new career as a senior data scientist at Novo Nordisk, a well-known company in the field of pharmaceutical and healthcare.
