Yoona Choi and Jung-Hyun Won attended the 65th Annual Meeting & Exposition of the American Society of Hematology (ASH), which took place from December 9 to 12, 2023, in San Diego, USA. Annually, the ASH conference brings together healthcare professionals, researchers, clinicians, and academics from around the globe to explore advancements in hematology. This prestigious event not only focuses on the study, diagnosis, treatment, and prevention of blood disorders but also features a variety of social events and opportunities for collaboration and the sharing of insights among experts in the field. Official photos from ASH are available here.

At the 'Outcomes Research—Lymphoid Malignancies: Poster II' session, Yoona Choi, serving as the first author and representing the entire research team, presented a study titled 'Evaluating the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Pediatric Acute Lymphoblastic Leukemia: A Common Data Model-Based Real-World Data Approach.' This study investigated steroid pretreatment in pediatric acute lymphoblastic leukemia (ALL) patients undergoing remission induction chemotherapy (RIC), with the aim of reducing complications such as tumor lysis syndrome (TLS). Analyzing data from Seoul National University Hospital and Catholic University Seoul St. Mary’s Hospital, the study compared patients who received steroid pretreatment at least seven days before RIC with those who did not. The findings indicated a significant reduction in TLS incidence in the pretreated group. While overall survival rates were higher in this group, the difference was not statistically significant. These results provide important real-world evidence for the effectiveness of steroid pretreatment in reducing TLS risk in pediatric ALL patients.

This study was part of a larger project, funded by a grant from the Ministry of Health and Welfare, aiming to develop a disease registry database for pediatric ALL using real-world data in the clinical data warehouse (CDW) of Seoul National University Hospital and Catholic University Seoul St. Mary’s Hospital, thereby generating real-world evidence for pediatric ALL. 

Yesol Hong won the Excellent Research Award at the 30th Annual Conference of the Korean Society for Clinical Pharmacology & Therapeutics (KSCPT), December 1-3, 2022, held at Bexco, Busan, South Korea. The title of YeSol's presentation was "One-year follow-up of patients with COVID-19: Analysis of new-onset disease after COVID-19 diagnosis using Real-World Data". She discussed the results of an analysis of the occurrence of disease in patients with COVID-19 over a year using two different real-world data sets. One of her analysis results was a nation-wide study, which was significant in that it included the largest number of study subjects among the studies analyzing the post-COVID-19-condition in Korea. Jung Hyun Won equally contributed to this study with Professor Howard Lee serving as the principal investigator.

Additionally, Siun Kim, Yoona Choi, David Seung U Lee also presented their research through poster exhibition at the conference. 

Full presentations can be watched at the following YouTube links: 

CCADD is breaking the ground for biomedical natural language processing or NLP!

On January 22^nd, 2021, CCADD was nominated by the Ministry of Food and Drug Safety to develop an NLP model that extracts core clinical information from unstructured data in the Korea Adverse Event Reporting System (KAERS). The total research fund amounts to two hundred million Korean won or a hundred and eighty thousand U.S. dollars. The project will be performed in collaboration with the Machine Intelligence lab in the Department of Electrical and Computer Engineering, Seoul National University (Prof. Gyo-Min Jung). 

For starters, CCADD will build a manually annotated dataset, which is indispensable to train and refine any machine learning algorithm including NLP. Next, CCADD plans to build the KAERS-BERT model, an NLP model specifically tuned to handle adverse drug events. The KAERS-BERT model will be pre-trained on KAERS text data, which then will be deployed to extract medical information from free texts, such as hand-written records of adverse events. Once complete, the KAERS-BERT model is expected to address many of the language ambiguities caused by the mixed use of English and Korean in medical domains already peppered with technical and medical jargons. The KAERS-BERT model will significantly improve the performance and the outcome of NLP tasks when extracting information from clinical notes written in English and Korean. 

Prof. Howard Lee, the PI of the grant, congratulated the team, saying "The grant awarding is timely and important since it will help CCADD enter a new area of biomedical NLP." At the conclusion of this project, CCADD will once again have secured its place as the leading lab in the field of convergence science between medicine, drug development, and machine learning including NLP. "This certainly provides CCADD with a momentum to transform into a frontrunner in the field", Prof. Lee added.