Past Research
가정간호 노트: 전당뇨 증상 자연어 분석
연구과제명
가정간호 노트를 이용한 전당뇨 증상 자연어 분석 알고리즘 개발 및 평가를 통한 지역사회 내 당뇨 고위험군 환자 조기 발견
Early detection of pre-diabetes high-risk patients using homecare nursing notes
연구기간
2021.03.01~2024.02.29
주관처
과학기술정보통신부
연구목표
지역사회 당뇨 고위험군 환자 선별을 위한 전당뇨 증상 자연어 분석 알고리즘을 개발 및 평가하고, 이를 가정간호 간호 노트에 적용하여 당뇨 고위험군 환자 분류 및 환자 특성 파악을 통한 조기 발견을 가능하게 하는 것
[1단계 최종 목표]
– 전당뇨 환자 위험군 선별을 위한 전당뇨 증상 자연어 분석 알고리즘 개발 및 평가
[2단계 최종 목표]
-가정간호 노트에 전당뇨 증상 자연어 분석 알고리즘 적용하여 전당뇨 증상수 및 증상 빈도에 따라 고위험군 환자 분류
[3단계 최종 목표]
-EHR 데이터 연계를 통한 당뇨 고위험군 환자의 개인적, 사회적 및 임상적 특성 파악
Publication
Published:
– Jeon E, Kim A, Lee J, Heo H, Lee H, Woo K. Developing a Classification algorithm for Prediabetes Risk Detection from Home care Nursing Notes: Using Natural Language Processing. CIN: Computers, Informatics, Nursing. 2023;41(7):539-547. Available from: https://dx.doi.org/10.1097/CIN.0000000000001000 (Jean E. and Kim A. contributed equally to this work.) ▶ Link
– Kim A, Jean E, Lee H, Heo H, Woo K. Risk factors for prediabetes in community-dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights. Research in Nursing & Health, 2024;47(6):620-634. ▶ Link
Under Review:
– 이외 1편 저널 리뷰 중
Early Detection of Diabetes High-risk Patient Using Homecare Nursing Notes
- GRANT SUPPORT -
2021 – 2023
National Research Foundation of Korea (NRF) Early Career Grant
Title of Project: Early Detection of Diabetes High-risk Patients Using Homecare Nursing Notes
Goal: This study aims to develop and validate a natural language processing algorithm for detecting prediabetes symptoms and identifying high-risk patient characteristics through EHR data linkage.
Funding Agency: South Korea Ministry of Science and ICT
Role: Principal Investigator
Exploring the Association Between Caregiver Education on Diabetes
Self-management in the Community/at Home and Patient Hospitalization
- GRANT SUPPORT -
2020 – 2022
Korea Research Resettlement Fund
Title of Project: Exploring the Association Between Caregiver Education on Diabetes Self-management in the Community/at Home and Patient Hospitalization
Goal: This study aims to examine the effect of caregiver involvement in type 2 diabetes mellitus education within a community and patient diabetes care outcomes through a systematic literature search and meta-analysis.
Funding Agency: Seoul National University
Role: Principal Investigator
Exploring Prevalence of Wound Infections and Related Patient Characteristics in Homecare Using Natural Language Processing
- GRANT SUPPORT -
2019 – 2020
Eugenie and Joseph Doyle Research Partnership Fund
Title of Project: Exploring Prevalence of Wound Infections and Related Patient Characteristics in Homecare Using Natural Language Processing
Goal: This study aims to link Natural Language Processing (NLP) findings to a routinely collected data in homecare (Outcome and Assessment Information Set, OASIS) to capture important, under-reported critical patient information. The investigators will use NLP findings for wound infection-related information from clinical notes and link to a structured data (OASIS) to estimate the prevalence of wound infections and describe related patient characteristics in the homecare population.
Funding Agency: Center for Home Care Policy & Research, Visiting Nurse Service of New York
Role: Principal Investigator
Co-Investigators: Dr. Jingjing Shang, Dr. Maxim Topaz
Exploring Prevalence of Wound Infections and Related Patient Characteristics in Homecare Using Natural Language Processing
- GRANT SUPPORT -
2019 – 2020
Columbia University School of Nursing Intramural Pilot Grant
Title of Project: Exploring Prevalence of Wound Infections and Related Patient Characteristics in Homecare Using Natural Language Processing
Goal: This study aims to use advanced Natural Language Processing (NLP) methods to create and validate an NLP algorithm to extract wound infection-related information from clinical notes (~2.6 million) in the homecare population.
Funding Agency: Columbia University School of Nursing
Role: Principal Investigator
Co-Investigators: Dr. Maxim Topaz, Dr. Jingjing Shang