A5100699921- Machine Learning in Healthcare
- Cardiac, Anesthesia and Surgical Outcomes
- Hemodynamic Monitoring and Therapy
- Emergency and Acute Care Studies
- Blood Pressure and Hypertension Studies
- Healthcare Operations and Scheduling Optimization
- Topic Modeling
- Bioinformatics and Genomic Networks
- Healthcare Policy and Management
- Artificial Intelligence in Healthcare and Education
- Health Systems, Economic Evaluations, Quality of Life
- Artificial Intelligence in Healthcare
- Consumer Perception and Purchasing Behavior
- Enhanced Recovery After Surgery
- Natural Language Processing Techniques
- Law, AI, and Intellectual Property
- Generative Adversarial Networks and Image Synthesis
- Advanced Graph Neural Networks
- Biomedical Text Mining and Ontologies
- Industrial Vision Systems and Defect Detection
- Traditional Chinese Medicine Studies
- Aortic Thrombus and Embolism
- Delphi Technique in Research
- Artificial Intelligence in Law
- Chronic Disease Management Strategies
Asan Medical Center
2021-2025
University of Ulsan
2021-2024
Ulsan College
2021-2024
Jeonbuk Development Institute
2012
Clinical trial evidence supports early low-density lipoprotein cholesterol (LDL-C) goal achievement in patients with atherosclerotic cardiovascular disease (ASCVD), but real-world Asia is lacking. We investigated the effects of LDL-C on recurrent major events (MACEs) among very-high-risk ASCVD South Korea. included adult hospitalized (acute coronary syndrome [ACS], stable angina, ischemic stroke, transient attack, peripheral arterial disease, or asymptomatic artery disease) at a Korean...
Abstract Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed develop useful and inexpensive tool derived from electronic medical records that supports clinical decision-making can be easily utilized by department physicians. We presented machine learning models predicted the likelihood hospitalizations within 24 hours estimated waiting times. Moreover, we revealed enhanced performance these compared existing incorporating...
This mixed-methods study investigates the design and instructional practices of massive open online courses (MOOCs) instructors within learning environment to address cultural diversity learner personalization needs. Leveraging a grounded theory approach, researchers analyzed two rounds email interviews (n1= 25; n2=19) with MOOC education leaders about sensitivity in MOOCs. Those led formation 30-item questionnaire completed by 152 instructors. While many sample did not fully grasp complex...
Background Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing optimal treatment time. The use hospital processes requires effective bed management; a stay that is longer than time hinders management. Therefore, predicting patient’s hospitalization period may support making judicious decisions regarding Objective First, this study aims to develop machine learning...
As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized dosage. Adverse drug events(ADE) resulting from overdose can be critical, so that typically physicians adjust the dosage through INR monitoring twice week when starting warfarin. Our study aimed develop machine learning (ML) models predicts discharge of as initial using clinical data derived electronic medical records within 2 days hospitalization....
Abstract Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support advance previous methods predict prognosis of patients in network models. This study aims address challenge implementing complex highly heterogeneous dataset, including following: (1) demonstrating build multi-attributed multi-relational graph model (2) applying downstream disease prediction task...
<title>Abstract</title> Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features patient status at single time point. We developed self-attention-based predict within 3 years from series data utilizing numerous in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient through code mapping feature selection top...
Large language models (LLMs) have exhibited outstanding performance in natural processing tasks. However, these remain susceptible to adversarial attacks which slight input perturbations can lead harmful or misleading outputs. A gradient-based defensive suffix generation algorithm is designed bolster the robustness of LLMs. By appending carefully optimized suffixes prompts, mitigates influences while preserving models' utility. To enhance understanding, a novel total loss function...
Developing large-scale language models (LLMs) for health care requires fine-tuning with domain data suitable downstream tasks. However, LLMs medical can expose the training used during learning to adversarial attacks. This issue is particularly important as contain sensitive and identifiable patient data. The prompt-based attack approach was employed assess potential privacy breaches in LLMs. success rate of evaluated by categorizing 71 questions into three key metrics. To confirm exposure...
<title>Abstract</title> Background Predicting the length of stay in advance will not only benefit hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality care. More importantly, understanding severe patients who require general anesthesia is key enhancing health outcomes. Objective Here, we aim discover how machine learning can support resource allocation management resulting from prediction. Methods A retrospective cohort...
Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur from actual patients.
Background Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction entire crucial, predicting at a detailed level, such as specific wards rooms, more practical useful scheduling. Objective The aim of this study was to develop web-based support tool that allows administrators grasp each ward room according different time periods. Methods We trained time-series...
Predicting the length of stay in advance will not only benefit hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality care. More importantly, understanding severe patients who require general anesthesia is key enhancing health outcomes. Here, we aim discover how machine learning can support resource allocation management resulting from prediction. A retrospective cohort study was conducted January 2018 October 2020. total...
지역통계는 특성화통계를 중심으로 그 수요가 증가하고 있음에도 불구하고 통계작성비용 증가 등의 이유로 인하여 조사통계로 모든 요구를 충족시키기에는 한계가 있다. 이에 대안으로 고려되는 것이 행정통계이다. 본 연구에서는 전라북도 행정통계 자료 가운데 지역정책 수립 및 특성화에 기여하는 대표통계를 선정하고 이를 체계적으로 관리함으로써 질적 향상을 통한 신뢰성 확보를 이룰 수 있는 방안에 대해서 논하였다. 결과 45개 항목을 대표통계로 최종 선정하였다. 연구에서 대표통계의 선정에 대한 필요성을 제기하고 구체적인 선정절차를 밝힌 것은, 연구가 지방자치단체에서 생산되는 행정통계의 체계적인 관리와 효율적인 활용에 도움을 줄 계기가 되기를 기대 해서이다. In spite of growing demand for the region specific statistics, due to increase in cost making out statistics and other reasons,...
<sec> <title>BACKGROUND</title> Understanding the length of stay severe patients who require general anesthesia is key to enhancing health outcomes. </sec> <title>OBJECTIVE</title> Here, we aim discover how machine learning can support resource allocation management and decision-making resulting from prediction. <title>METHODS</title> A retrospective cohort study was conducted January 2018 October 2020. total 240,000 patients’ medical records collected. The data were collected exclusively...
<sec> <title>BACKGROUND</title> As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized dosage. Adverse drug events resulting from overdose can be critical. Our study aimed develop machine learning (ML) model that predicts the appropriate discharge dosage of using electronic medical records large hospital. Additionally, we externally validated ensure its accuracy. </sec> <title>OBJECTIVE</title> This...
<sec> <title>BACKGROUND</title> Transformer-based language models have shown great potential to revolutionize health care by advancing clinical decision support, patient interaction, and disease prediction. However, despite their rapid development, the implementation of transformer-based in settings remains limited. This is partly due lack a comprehensive review, which hinders systematic understanding applications limitations. Without clear guidelines consolidated information, both...
<sec> <title>BACKGROUND</title> Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction entire crucial, predicting at a detailed level, such as specific wards rooms, more practical useful scheduling. </sec> <title>OBJECTIVE</title> The aim of this study was to develop web-based support tool that allows administrators grasp each ward room according different time...