Intelligence and Control-based BioMedicine

지능 및 제어 기반 생명의학

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Our mission is to utilize and integrate data-based and knowledge-based models to define and solve various meaningful problems in medicine and biology. We deal with various types of data, including time series. To achieve this, we utilize diverse methodologies, including statistics, control, optimization theory, as well as machine learning and artificial intelligence. From the science of discovering knowledge to the engineering of proposing new applications, we aim to address not only the unmet demands in clinical medicine and biology but also to identify and solve undiscovered needs. We explore in the areas of methodology research and practical field applications, ranging from prediction and inference to decision-making and implementation in the biomedical sciences and clinical fields.

Major research field

Medical Data Science&Medical Informatics, Machine Learning&Artificial Intelligence in Biomedicine, Optimization&Control theory and its Medical App

Desired field of research

Medical Data Science&Medical Informatics, Machine Learning&Artificial Intelligence in Biomedicine, Optimization&Control theory and its Medical App

Research Keywords and Topics

· Integration of data-driven and knowledge-based models and methods in biomedical fields
· From biomedical science and engineering to clinical applications including prediction, prognosis, interventions, and decision making.
· Explainability and causality in prediction and control models for translational medicine
· (Clinical) domain knowledge distillation from and injection to data-driven and knowledge-based models
· Digital Healthcare and Literacy for Patients and Physicians

Research Publications

· Computer Methods and Programs in Biomedicine / A Practical Approach based on Learning-based Model Predictive Control with Minimal Prior Knowledge of Patients for Artificial Pancreas / M. H. Lim; et al. / 2023-06
· IEEE Journal of Biomedical and Health Informatics / Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics / M. H. Lim; et al. / 2022-09
· IEEE Access / A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation / M. H. Lim; et al. / 2021-07
· Stroke / Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke / W. H. Lee, M. H. Lim; et al. / 2020-02

Patents

· Surgical robot system based on Headset using voice recognition Microphone
· Method for providing information for predicting dysphagia in patients with stroke
· Apparatus and method for symptom and disease management based on learning
· Online-based healthcare method and apparatus

국가과학기술표준분류

  • LC. 보건의료
  • LC04. 치료·진단기기
  • LC0403. 지능형 판독시스템

국가기술지도분류

  • 건강한 생명사회 지향
  • 021100. 생체신호처리기술

녹색기술분류

  • 녹색기술관련 과제 아님

6T분류

  • BT 분야
  • 보건의료 관련응용
  • 020217. 의과학?의공학 기술