Yunlong Wang

At IQVIA, I am a lead research scientist in Advanced Analytics where I lead a team of data/research scientists, software engineers and research interns. Leveraging state of art machine learning and NLP techniques, we develop algorithms and software for healthcare data analytic projects such as disease detection, disease progression prediction, medical entity embedding, sports data analysis, and patient volume forecasting.

Prior joining IQVIA in 2016, I got my Ph.D. in ECE from Stony Brook University in 2015 and then joined the digital technology center at the University of Minnesota as a post-doctoral researcher for a year. My research interests lie in areas of Bayesian methods, machine learning, distributed signal processing, as well as their applications in healthcare and social network data analytics. In particular, using electronic medical record data, below are some of my ongoing research works:

• Disease detection: identify the potentially un-diagnosed patients who have the disease
• Disease progression prediction: predict changes in patient journey, e.g., treatment initiation.
• Patient sub-typing: Segmentation over patients for precise medicine
• Data mining over graph, e.g., patient-doctor networks

News:

• 01/2020: I will present “Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks: A Novel Approach for Orphan Drug Physician Targeting” at the 2020 Pharmaceutical Management Science Association (PMSA 2020) Winter Symposium to be held on Jan 16-17, 2020, in Las Vegas, Nevada.
• 01/2020: Yue Wang will present our work on applying non-negative matrix factorization to patient sub-typing task at Pacific Symposium on Biocomputing (PSB 2020), Hawaii, 2020. "Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning". [PDF]
• 12/2019: Our work “Exploiting Interconnection with Dynamical System Information to Enhance Forecasting Accuracy by Gaussian Processes in Real Word Application”, has been selected for presentation at the 2020 Pharmaceutical Management Science Association (PMSA 2020) Annual Conference to be held on May 17-20, 2020, in Louisville, Kentucky.
• 12/2019: Our paper “A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Patients from Nonalcoholic Fatty Liver Disease Patients using Electronic Medical Records”, has been accepted for poster presentation the Workshop on Health Intelligence at the 34th AAAI Conference on Artificial Intelligence (AAAI-20) on February 7th, 2020 in New York, New York.
• 12/2019: We have three workshop paper presented at Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) on meta learning, representation learning, and federated learning.
• 11/2019: I give a presentation "Learning from Electronic Health Records: Use Cases of Machine Learning for Clinical Data Analytics ", at the Electrical and Computer Engineering department of University of Virginia.
• 08/2019: Dr. Christina Mack and I present our work "Detecting Rare Diseases: A Case Study Using Machine Learning Semi-supervised Networks To Identify Under-diagnosed Patients " at the 35th International Conference on Pharmacoepidemiology & Therapeutic Risk Management (ICPE 2019) Annual Conference held on August 24-28, 2019 in Philadelphia, Pennsylvania, USA.