Yonghui Wu is an assistant professor in the College of Medicine, Department of Health Outcomes and Biomedical Informatics. Yonghui's research interests include natural language processing (NLP) and machine learning. His research has contributed substantially to clinical and biomedical NLP – including information extraction from clinical notes and biomedical literature, word sense disambiguation (WSD) for ambiguous biomedical terms; predictive modeling for drug adverse reactions and drug new indications (known as drug repurposing); various applications to apply NLP and machine learning to solve clinical and translational problems.
Areas of Expertise (4)
Natural Language Processing
Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: An HCV-TARGET analysis.Hepatology
Haesuk Park, et. al
We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection. We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics and virologic data.
Procedural complications associated with invasive diagnostic procedures after lung cancer screening with low-dose computed tomography.Lung Cancer
Although the National Lung Screening Trial (NLST) has proven low-dose computed tomography (LDCT) is effective for lung cancer screening, little is known about complication rates from invasive diagnostic procedures (IDPs) after LDCT in real-world settings. In this study, we used the real-world data from a large clinical research network to estimate the complication rates associated with IDPs after LDCT.
Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.Journal of the American Medical Informatics Association
Jiang Bian, et. al
There has been a surge of national and international clinical research networks (CRNs) curating immense collections of real-world data (RWD) from diverse sources of different data types such as electronic health records (EHRs) and administrative claims among many others. One prominent CRN example is the national Patient-Centered Clinical Research Network (PCORnet) funded by the Patient-Centered Outcomes Research Institute (PCORI) that contains more than 66 million patient data across the United States.
Assessing mental health signals among sexual and gender minorities using Twitter data.Health Informatics Journal
Yunpeng Zhao, et. al
Sexual and gender minorities face extreme challenges that breed stigma with alarming consequences damaging their mental health. Nevertheless, sexual and gender minority people and their mental health needs remain little understood. Because of stigma, sexual and gender minorities are often unwilling to self-identify themselves as sexual and gender minorities when asked. However, social media have become popular platforms for health-related researches.