题目:Machine Learning for Disease Risk Stratification, Online Adverse Events Extraction and Financial Credit Scoring 2021-06-08 题目:Machine Learning for Disease Risk Stratification, Online Adverse Events Extraction and Financial Credit Scoring 主讲人：周建栋 时间：2021年6月15日15：00-18:00 地点：管理学院315会议室 腾讯会议号：400 583 858 讲座介绍： Recent investigations of novel machine learning techniques were developed for individualized disease risk stratification, adverse events extraction and financial credit scoring will be presented. Firstly, we incorporated latent patterns between clinical characteristics of patients with Brugada Syndrome using nonnegative matrix factorization to improve risk stratification of severe arrhythmic outcomes. Secondly, we developed a novel semantic and sentiment enriched multi-task bidirectional LSTM deep learning model (S2-Multitask-BiLSTM) to extract marijuana-related adverse events from massive social media texts for public health safety surveillance. Thirdly, we proposed an intrinsically interpretable factorization machine model for credit scoring. The model is able to capture the nonlinear interaction patterns among features, handle data sparsity, and efficiently deal with data imbalance issues with an asymmetric non-convex -loss function. Interpretation with visualizations provides risk management agents with useful tools to observe the prediction strengths of both individual features and pair-wise feature interactions. 主讲人介绍: 周建栋，香港城市大学数据科学学院数据科学专业在读博士，现阶段主要研究方向包括可解释人工智能，计算医疗和金融科技等。以第一作者(或共同第一作者)身份在Gut, IEEE Transactions on Fuzzy Systems, Transportation Research Part B: Methodological, Information Sciences, Journal of the American Heart Association, Journal of Hypertension, Pharmacological Research, European Journal of Clinical Investigation, Heart Rhythm, Frontiers in Physiology等SCI/SSCI JCR Q1国际主流期刊上发表论文15余篇，同时有多篇论文在INFORMS Journal on Computing，European Journal of Operational Research, Industrial Management & Data Systems, Information Sciences, Cardiovascular Drugs and Therapy等期刊审稿及获得返修意见，曾多次在国际高水平会议做论文报告。