Symposium 4 - APRC 2024

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Symposium 4：Application of Intelligent Medicine in Critical Care

Recent exponential growth in computing power and portability, the power of artificial intelligence (AI) became available in ICU where data are vast, abundant, and complex. Many research topics in critical care medicine have employed the concept of AI to recognize hidden disease patterns among the extremely heterogeneous and noise clinical datasets. AI models might provide useful solutions in disease detection, phenotyping, and prediction that might alter the course of critical diseases. They may also lead to optimal, individualized treatment strategies when multiple treatments options exist. but our understanding of the power and utility of AI in critical care medicine is still quite limited. In addition, there are many obstacles and pitfalls for AI to overcome before becoming a core component of our daily clinical practice. In this symposium, we seek to introduce the roles of intelligence medicine in the critical care medicine.

Date: Saturday, April 27, 2024 | Time: 08:30-10:00 (GMT+8)

Moderator: Dr. Chieh-Liang Wu (Taiwan) | Dr. Kuo-Chin Kao (Taiwan) | Dr. Chang-Wen Chen (Taiwan)

Time (GMT+8)	Topic	Speaker	Country / Region
08:30-09:00	Digital Transformation Saves More Life	Dr. Wei-Cheng Chen	Taiwan
09:00-09:30	Explainable Machine Learning to Assist Weaning from Mechanical Ventilation in Critical Care	Dr. Wen-Cheng Chao	Taiwan
09:30-10:00	Developing a Smart ICU	Prof. Ming-Ju Tsai	Taiwan

Developing a Smart ICU

Abstract:
The modern healthcare landscape is witnessing a paradigm shift with the integration of cutting-edge technology into traditional medical practices. Among these innovations, the development of Smart Intensive Care Units (ICUs) emerges as a groundbreaking advancement in patient care.

Smart ICUs leverage a network of interconnected devices, sensors, and advanced analytics to continuously monitor patients, gather real-time data, and provide predictive insights. By integrating artificial intelligence and machine learning algorithms, these systems enable early detection of deterioration, reducing the likelihood of adverse events and facilitating timely interventions. Moreover, Smart ICUs streamline workflows through automated documentation, smart alarms, and predictive analytics, allowing healthcare professionals to focus more on patient care and less on administrative tasks.

This presentation explores the evolution, components, and potential impact of smart ICUs in facilitating patient care. The experience of developing a smart ICU in Kaohsiung Medical University Hospital will also be shared. We believe that the development of smart ICUs represents a transformative approach to critical care management, promising to enhance patient outcomes, improve operational efficiency, and drive innovation in the healthcare industry.

Prof. Ming-Ju Tsai

Attending Physician, Professor | Kaohsiung Medical University Hospital, Kaohsiung Medical University
Taiwan

Professional Experience:

Prof. Ming-Ju Tsai obtained his M.D. and Ph.D. degrees from Kaohsiung Medical University. He is currently an attending physician in Kaohsiung Medical University Hospital and professor in Kaohsiung Medical University. His study interests include critical care, airway diseases, sleep disorders, big-data analysis, and smart healthcare. He tries to figure out the pathophysiological mechanisms of airway diseases using multi-omics approach. He also devotes himself in improvement and innovation of hospital information system through establishing and deploying artificial intelligence models in clinical practice, in order to improve patient care, safety, and efficiency.

Explainable Machine Learning to Assist Weaning from Mechanical Ventilation in Critical Care

Abstract:
The successful implementation of artificial intelligence (AI) in critical care hinges on the interpretability of AI models and their integration with clinical workflows. We utilized data from the Taichung Veterans General Hospital in Taiwan (2015–2019) and employed various ML models including XGBoost, CatBoost, LightGBM, RF, and LR to establish the extubation prediction model. Additionally, we used visualized explainable AI tools, such as SHapley Additive exPlanations (SHAP), partial dependence plot (PDP) and Local Interpretable Model-agnostic Explanations (LIME) plots, to provide understandable and transparent model predictions. These tools align AI outcomes with clinical insights, enhancing trust and usability in a critical care setting. Our results indicate that the explainable AI models not only achieve high accuracy in predicting extubation outcomes but also are integrated into the clinical workflow, thereby advancing the practical application of AI in medical settings. The study showcases the potential of AI to augment clinical decision-making processes, particularly in the complex and dynamic environment of intensive care units.

Dr. Wen-Cheng Chao

Chief of Medical Division, Department of Critical Care Medicine | Taichung Veterans General Hospital
Taiwan

Professional Experience:

I am an intensivist with a keen interest in critical databases and their application in medical research. I have worked extensively with several critical databases, including the TCVGH database, Taiwan Severe Influenza Research Consortium (TSIRC), National Health Insurance Research Database in Taiwan, eICU, and MIMIC IV, to extract valuable insights into patient care and outcomes. I am also interested in the field of explainable machine learning and its potential to improve clinical decision-making. In addition, I has conducted research on the whole transcriptome in septic patients and to explore the molecular mechanisms, particularly immunological features, in patients with sepsis.

Digital Transformation Saves More Life

Abstract:
In an era of rapid change and unprecedented healthcare challenges, “Digital Transformation Saves More Lives” highlights the critical need for adopting digital tools, like seamless data integration, big data analytics, and artificial intelligence applications in healthcare. These are essential, not optional, for enhancing care quality amid resource constraints. They streamline operations, reduce errors, and personalize patient care, demonstrating their value through real-world applications. For example, drug-resistant pathogens pose a global crisis, with the WHO reporting that over 10 million people could die annually from infections due to antimicrobial resistance in 2050. i.A.M.s in CMUH using AI predictive analytics can identify drug-resistance pathogens, allowing dcotors to adjust antibiotics earliler with optimized dosage. This approach is not only more efficient but also proactive. Emphasizing the automation of routine tasks, this presentation highlights how digital transformation optimally reallocates resources, ensuring that healthcare professionals focus where they’re needed most. It will explore how embracing these technologies can transform healthcare delivery, ultimately saving more lives.

Dr. Wei-Cheng Chen

Chief Secretary, CMUH; attending physician of division of pulmonary and critical care medicine, CMUH; assistant professor of school of medicine, CMU | China Medical University Hospital
Taiwan

Professional Experience:

Dr. Wayne WC Chen is a specialist in Pulmonary and Critical Care Medicine, with a focus on critical care medicine and interdisciplinary team care, particularly in areas such as antimicrobial resistance, acute respiratory distress syndrome (ARDS), and sepsis. He is involved in the development of smart healthcare in the CMUH, constructing the I.A.M.S for early diagnosis and prediction of antimicrobial resistance in sepsis, using the correct antibiotics and optimized dosages; for ARDS, he utilizes data integration, visual presentation, and AI detection to guide the team in implementing lung-protective strategies to improve clinical outcomes.

He is dedicated to combining medical expertise, interdisciplinary team collaboration, and smart healthcare to enhance clinical care.

Dr. Chieh-Liang Wu

Attending Physician | Taichung Veterans General Hospital
Taiwan

Professional Experience:

Chieh Liang Wu, MD, Ph.D., is the deputy superintendent of Taichung Veterans General Hospital (TCVGH). After graduating from National Yang-Ming Medical College in 1988, he completed his fellowship training in pulmonary and critical care medicine in 1997. He earned his Ph.D. in Life Science from National Chung Hsing University, Taiwan, in 2009. He is a clinical specialist in pulmonary infection and critical care medicine and also an expert in patient safety & quality and artificial intelligence in medicine. More than 90 scientific papers are published in pulmonary and respiratory care, critical care medicine, patient safety and quality, and artificial intelligence in medicine.

Dr. Kuo-Chin Kao

Professor | Chang Gung Memorial Hospital, Linkou
Taiwan

Professional Experience:

Dr. Kuo-Chin Kao is President of Taiwan Society of Critical Care Medicine (TSCCM) and Professor of Department of Thoracic Medicine, Chang Gung Memorial Hospital, Linkou and Department of Respiratory Therapy, College of Medicine, Chung Gung University. His interests are focused on the Acute Respiratory Distress Syndrome (ARDS) including clinical, basic and translational research to investigate new approach for ARDS through a systems medicine method. He is one of the leaders of Lung Transplantation Team in Chang Gung Memeorial Hospital, Linkou, which is most lung transplantation cases performed in Taiwan recently. Professor Kao is the reviewers of several critical care and respiratory journals and has published more than 150 scientific articles.

Dr. Chang-Wen Chen

Director of Pulmonary and Critical Care Medcine | National Cheng Kung University Hospital
Taiwan

Professional Experience:

Dr. Chen is currently the Director of Pulmonary and Critical Care Medicine in National Cheng Kung university Hospital (NCKUH) and Associate Professor of Internal Medicine, National Cheng Kung University, Tainan, Taiwan.

After graduating from Taipei Medical college, he completed residency training in Internal Medicine and Chest Medicine in NCKUH and obtained his master degree in bioengineering in Institute of Bioengineering, National Cheng Kung University in 1997. He was a short term fellow in Kyoto University and spent one year as research fellow in Institute of Physiology and Biophysics, University of Calgary, Canada in 2003.

Dr. Chen works as a complete intensivist since 2005. He is a member of Taiwan Society of Critical Care Medicine, Pulmonary and Critical Care Medicine and Emergency & Critical Care Medicine. He is also a member of European Society of Intensive Care Medicine. He joined the PLUG working group. He has original articles, case reports, and review papers published in peer review journals covering research fields of diaphragm microcirculation, critical care and ventilator physiology.

He is interested in respiratory mechanics, patient ventilator interaction and electrical impedance tomography.

Symposium 4：Application of Intelligent Medicine in Critical Care

APRC 2024 Secretariat

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