ISysE / GSDS 세미나가 다음과 같이 진행될 예정입니다.

# 날짜/시간: 2024년 5월 28일 화요일 16:00~17:00

# 장소 : E-2동 1501호

# 연사 : Professor Hyojung Kang / Kinesiology & Community Health / College of Applied Health Science / University of Illinois at Urbana-Champaign

# 제목 : Enhancing Healthcare Delivery and Outcomes through Data Science

# 초록 : With the proliferation of data and advancements in technology, the convergence of healthcare and data science presents opportunities for transforming patient care and enhancing health outcomes. This seminar explores the various applications of data analytics and AI methodologies in facilitating decision-making support for interventions targeting individuals with chronic conditions. Through ongoing case studies, we explore predictive analytics in identifying long-term medication usage patterns and adherence behaviors among diabetes patients. Additionally, we showcase the utilization of temporal-spatial models to pinpoint geographical areas that need interventions, while assessing the influence of socioeconomic factors on disease incidence rates. Finally, we discuss how data-driven approaches can help extract meaningful insights, optimize treatment strategies, and personalize interventions.

 

# 본 세미나는 영어로 진행됩니다.

 

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ISysE/GSDS dept. office invites you to the following seminar.

 

# Time/Date : May 28, 2024 (Tue.) 16:00~17:00

# Location : Room 1501 in E-2 building 

# Presenter : Professor Hyojung Kang / Kinesiology & Community Health / College of Applied Health Science / University of Illinois at Urbana-Champaign

# Title : Enhancing Healthcare Delivery and Outcomes through Data Science

# Abstract : With the proliferation of data and advancements in technology, the convergence of healthcare and data science presents opportunities for transforming patient care and enhancing health outcomes. This seminar explores the various applications of data analytics and AI methodologies in facilitating decision-making support for interventions targeting individuals with chronic conditions. Through ongoing case studies, we explore predictive analytics in identifying long-term medication usage patterns and adherence behaviors among diabetes patients. Additionally, we showcase the utilization of temporal-spatial models to pinpoint geographical areas that need interventions, while assessing the influence of socioeconomic factors on disease incidence rates. Finally, we discuss how data-driven approaches can help extract meaningful insights, optimize treatment strategies, and personalize interventions.

 

# The seminar will be in English