Imjin Ahn
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View article: Cardiovascular Outcomes of Early LDL-C Goal Achievement in Patients with Very-High-Risk ASCVD
Cardiovascular Outcomes of Early LDL-C Goal Achievement in Patients with Very-High-Risk ASCVD Open
Early LDL-C goal achievement was associated with lower recurrent MACEs in patients with very-high-risk ASCVD in South Korea, especially in patients with ACS. These findings underscore the importance of early LDL-C management in improving c…
View article: Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation Open
We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defin…
View article: Task-Specific Transformer-Based Language Models in Health Care: Scoping Review
Task-Specific Transformer-Based Language Models in Health Care: Scoping Review Open
Background 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 impleme…
View article: Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study
Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study Open
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 for the entire hospital is crucial, predi…
View article: Are polypharmacy side effects predicted by public data still valid in real-world data?
Are polypharmacy side effects predicted by public data still valid in real-world data? Open
Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with …
View article: Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study (Preprint)
Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study (Preprint) Open
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 for the entire hospital is crucial, pred…
View article: Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: Machine Learning Model Development and Evaluation
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: Machine Learning Model Development and Evaluation Open
Background Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding…
View article: Task-Specific Transformer-Based Language Models in Health Care: Scoping Review (Preprint)
Task-Specific Transformer-Based Language Models in Health Care: Scoping Review (Preprint) Open
BACKGROUND 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 implem…
View article: Machine Learning Models to Predict the Warfarin Discharge Dosage Using Clinical Information of East Asian Inpatients: Retrospective Cohort Study (Preprint)
Machine Learning Models to Predict the Warfarin Discharge Dosage Using Clinical Information of East Asian Inpatients: Retrospective Cohort Study (Preprint) Open
BACKGROUND As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events resulting from warfarin overdose can be crit…
View article: Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: Machine Learning Model Development and Evaluation (Preprint)
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: Machine Learning Model Development and Evaluation (Preprint) Open
BACKGROUND Understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. OBJECTIVE Here, we aim to discover how machine learning can support resource allocation management and de…
View article: Heterogeneous graph construction and HinSAGE learning from electronic medical records
Heterogeneous graph construction and HinSAGE learning from electronic medical records Open
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 and advance the previous methods to predict the pro…
View article: Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients Open
Understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting …
View article: Heterogeneous graph construction and HinSAGE learning from electronic medical records
Heterogeneous graph construction and HinSAGE learning from electronic medical records Open
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 and advance the previous methods to predict the pro…
View article: Heterogeneous Semantic Graph Construction and HinSAGE Learning from Electronic Medical Records (Preprint)
Heterogeneous Semantic Graph Construction and HinSAGE Learning from Electronic Medical Records (Preprint) Open
BACKGROUND Graph representations learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records (EMR) datasets. Adapting the integration limits will support and advance the …
View article: Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study
Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study Open
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 the optimal treatment time. The use of hospital…
View article: Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study
Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study Open
Background When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imp…
View article: Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study (Preprint)
Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study (Preprint) Open
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 the optimal treatment time. The use of hospita…
View article: Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study (Preprint)
Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study (Preprint) Open
BACKGROUND When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple im…