The AI Summit New York 2019 has been promoted as "the only event globally to focus exclusively on the impact of AI in business", and that's not an exaggeration. Attracting >5,000 attendees from all of the world, of whom Prof. Howard Lee is one, the AI summit New York 2019 was featured by three exclusive tracks, i.e., the Deliver Stage for the visionary high-ranked leaders and chief officers, the Implement Stage for the senior strategists, the Develop Stage for the technical leaders. Additionally, there were four summits, each focusing on a specific business field or sector, i.e., finance, retail, healthcare and pharma, and sales and marketing. Lastly, but not the least, AI training academy, mostly introductory though, was provided. Truly, it was enlightening to listen and watch how AI has been implemented to leverage its core technology to maximize the efficiency and productivity of day-to-day business practices in many business areas from strategy planning to decision makings to operations to outcome tracking. 

Of the many talks and show case presentations, Prof. Lee was particularly hooked up with a high-level, inspiring, and thought-provoking presentation by Mr. Jeff McMillan, Chief Analytics and Data Officer at Morgan Stanley. What made his talk unique from many others was that Mr. McMillan helped the audience realize AI is not an objective, but business outcome is. According to him, too many AI has simply failed to deliver what it promised to provide either because it didn't actually do what it said it did, it worked but added no value, it added value but was too complicated to use, it was to expensive to implement, or it lacked the necessary data. Certainly, Mr. McMillan's talk was a wake-up call to many AI-porn geeks. So influenced by Mr. McMillan's talk, Prof. Lee has decided to deliver his own version of Mr. McMillan's presentation after he returns. 

New York City in December was cold and wet as usual, mixed with businessmen, tourists, and passers-by with a motionless face. Prof. Lee has departed for the next destination, Boca Raton, Florida to attend another meeting there. That's is why he had to bring a much bigger suitcase than usual for this trip that suits two different temperature zones.   

Many of CCADD members have attended the Spring Conference of The Korean Society of Medical Informatics (KOSMI), held at Samsung Medical Comprehensive Cancer Center on June 14-15, 2018. Reflecting the recent drastic increase in the demand and importance of data science in health sciences, the theme of the conference was 'Evolving Data for Better Health'. More than 600 researchers, practitioners, and vendors in health informatics gathered together to share their knowledge, experience, results of research, and expertise through a series of keynote speeches, symposia, oral/poster presentations, and exhibitions.

Four abstracts by CCADD were accepted for poster presentation. Yoomin Jeon proposed a new approach to transform clinical trials eligibility criteria (EC, free-text) into structured data using the OMOP-CDM(Feasibility of Using the OMOP Common Data Model to Assess Clinical Trial Feasibility). She found that >75% of the EC could be mapped by the proposed approach, and the most important data group was condition.

Serin Lee, a student intern, gave her poster presentation entitled 'Dimensionality Reduction Model for Common Eligibility Criteria to effectively use Electronic Medical Record: A Pilot Study.' She proposed a dimensionality reduction model for common eligibility features (CEFs) based on ontology and semantic features of EC to reasonably compose the concept sets.

Dr. Soohyun Kim proposed a new type of command data model for clinical trials (Clinical Trial Common Data Model, CT-CDM) to support decision-making processes in clinical trials, entitled 'CDISC-based Common Data Model for Developing an Integrated Decision-Support System in Clinical Trials'. This command data model fully complies with the standards of Clinical Data Interchange Standards Consortium(CDISC) as a global, platform-independent data standard guaranteeing system interoperability. 

Lastly, but not the least, Dr. Jeong-An Gim shared his research findings: Application of Machine Learning Technology to Classify Healthy Subjects into Different Pharmacokinetic Exposure Groups to Tacrolimus. Using the decision-tree approach employed in R, Dr. Gim aimed to identify SNPs that are associated with the exposure to tacrolimus. The rs776746 in the CYP3A5 gene was the most influential mutation.

More detailed research findings can be downloaded below.

Much to everyone's delighted surprise, Serin Lee won an external solid-state drive of 1TB at the raffle draw! Since Serin will graduate in August, the prize was certainly a nice present. 

Lee H, Yim SV, Kim BH, Lee S. Comparative pharmacokinetic and bioequivalence evaluation of two formulations of morniflumate 350-mg tablets in healthy male subjects. Int J Clin Pharmacol Ther. 2017 Jan;55 (2017)(1):95-101.

Kim SW, JK Chung, DS Lee, C Kwark, JM Jeong, MK Kim, MC Lee, CS Koh, Lee H, KY Yoo, YO Ahn. Cutoff values of serum CEA in normal Korean adults and factors influencing serum CEA level, Kor J Nuclear Med, 1994; 28(3): 391-6.