Predicting peripartum blood transfusion: focusing on pre-pregnancy characteristics
본 연구는 임신 전 및 임신 중 위험 요인들이 분만 주기 수혈과 연관되어 있음을 확인했습니다. 확인된 요인 중 일부는 임신 전에 수정 가능하며, 본 연구는 분만 주기 수혈을 분류 도구로 검증했습니다.
Sung-Won Han
고려대학교 산업경영공학부
인공지능 및 데이터 분석 연구실 / Artificial Intelligence & Data Analytics Lab
swhan@korea.ac.kr
Development and Clinical Validation of an AI-Based Pain Management Algorithm
Development of Climate-Based Environmental Test Standards Using Customer Usage Data to Enhance Durability of New Technologies
Development of Trust AI Agents Core Model
Development of a model for predicting the composition of wheat flour using NIR analysis
Development of a model for predicting the physical properties of flour
English problem generation algorithm based on large language models (LLMs)
Question Generation Model Research Based on Artificial Intelligence
Automatic Generation Algorithm for Product Information Tagging using AI
Establishment of Optimized Medical Database and Development of Questionnaire AI Algorithm for Promoting Non-face-to-face Healthcare
Diagnosis of Spinal Fractures Using Artificial Intelligence
Artificial Intelligence Research for Monomer Design
Battlefield Data Simulation Technology (Battlefield Data Generative Model, Unstructured Simulated Battlefield Data Generation)
Big Data Group for Problem-defining Talent Training (BK21 Project)
Development of 6D Object Pose Estimation Model in Medical Environment
A Study on Artificial Intelligence Writing Technology using Natural Language Processing
Development of Questionnaire AI to Provide Remote Medical Consultations
A Study on the Development of Image Classification for Secondary Battery Products using Artificial Intelligence
Development of AI-based Process Risk Preliminary Review System
Quantum Machine Learning Simulator (advisory and student researcher participation)
Development of Genetic Analysis Methodology Combining High-Dimensional Networks and Artificial Intelligence (development of network model-based CNN and graph CNN deep learning algorithms)
Extraction of Durable Deteriorated Parts and Development of Future Failure Patterns Prediction using Claim Data (deep learning model development)
Development of CDM Extended Model for Infectious Disease Data and Development of a Tool to Utilize the Model
Development of CDM Extended Model of Unstructured Data based on Pathology, Electrocardiogram, and Echocardiography and Development of a Tool to Utilize the Model
Industrial Artificial Intelligence Professionals Training (opened Deep Learning Theory and Application course)
Technology to Predict and Control Nonlinear Characteristics of Artificial Intelligence-based Noise
Development of Efficient Performance Prediction Technology based on Big Data
Construction of a Prediction Model for Ship AIS Destination
Construction of Solar Power Forecasting Algorithm using Machine Learning
Development of Forecasting Program for the Weather Force of a Ship using the Performance Data and Weather Data
Development of Universal Chest X-ray Determination Assistance Technology based on Artificial Intelligence Convolutional Neural Network and Circular Neural Network
Development of a Crop Yield Prediction Model and building a Platform (development of deep learning algorithms for crop reading and quality prediction based on drone image data)
Numerical Weather Forecasting Model Data Frame Interpolation Service
Development of Failure Prediction Technology based on Claim Data [Deep Learning / Machine Learning] (Hyundai Motor Future Technology Research Project)
Development of an Algorithm to Support Efficient Operation of Ships to Overcome Marine Environmental Regulations (development of machine learning/deep learning algorithms)
Construction Service of Cloud Movement Vector Production Algorithm based on Chollian Satellite Image (development of cloud movement prediction algorithm using deep learning)
Development of Mathematical Methodology for Genetic Network Analysis under High-Dimensional Big Data
Development and Performance Improvement of Artificial Intelligence Algorithm for Arrhythmia Electrocardiogram Detection (deep learning algorithm development)
A Study on the Electrochemical Properties of Nanoparticles through Informatics Analysis
출처: 연구실 홈페이지
현재 재학생
13명
최근 5년 졸업
수집 중
본 연구는 임신 전 및 임신 중 위험 요인들이 분만 주기 수혈과 연관되어 있음을 확인했습니다. 확인된 요인 중 일부는 임신 전에 수정 가능하며, 본 연구는 분만 주기 수혈을 분류 도구로 검증했습니다.
본 연구는 허리둘레로 측정한 복부 비만이 일반 비만을 보정한 후에도 치매 위험의 유의한 증가와 관련이 있음을 보여줍니다.
본 연구는 낮은 알라닌 아미노전이효소(ALT) 기준값이 간 관련 부정적 결과를 예측하는 데 우수한 예측력을 보임을 전국 인구 기반 종단 코호트 연구를 통해 입증합니다.
본 연구는 유방암과 난소암 종양에서 DNA 복제수 변이(CNA)와 단백질 간의 조절 관계를 특성화하기 위해 ProMAP이라는 통계 방법을 제안합니다. 이 방법은 배치 효과와 단백질 데이터의 결측값 패턴을 고려하며, CPTAC-TCGA 데이터에 적용하여 8p11.21 영역을 포함한 여러 게놈 영역에서 CNA와 단백질 풍부도의 연관성을 규명했습니다.
본 논문은 두 개의 서로 다른 환자군의 유전자 네트워크를 추정하기 위해 jDAG 방법을 제안합니다. 이 방법은 공동 데이터셋에서 공통 방향 간선과 서로 다른 간선을 식별할 수 있으며, 시뮬레이션 연구에서 기존 방법보다 우수한 성능을 보입니다. ER+ 및 ER- 유방암 데이터셋 사례 연구를 통해 방법의 적용을 제시합니다.
논문 데이터가 수집되면 연구 키워드가 자동 추출됩니다