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» Authors » Hyeram Seo

Hyeram Seo

Explore the profile of Hyeram Seo including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 13
Citations 10
Followers 0
Related Specialties
Medical Informatics
Science
Health Services
Top 10 Co-Authors
Young-Hak Kim
Yunha Kim
Heejung Choi
Tae Joon Jun
Minkyoung Kim
Hansle Gwon
Imjin Ahn
Ha Na Cho
Gaeun Kee
Jiye Han
Published In
Sci Rep
JMIR Med Inform
Health Care Manag Sci
JMIR Public Health Surveill
Heliyon
Affiliations
Soon will be listed here.
Recent Articles
1.
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation
Cho H, Ahn I, Gwon H, Kang H, Kim Y, Seo H, et al.
BMC Med Inform Decis Mak . 2024 Nov; 24(1):350. PMID: 39563368
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....
2.
Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals
Kim Y, Kang H, Seo H, Choi H, Kim M, Han J, et al.
Sci Rep . 2024 Oct; 14(1):23443. PMID: 39379478
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 of...
3.
Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory
Choi H, Kim Y, Kang H, Seo H, Kim M, Han J, et al.
Sci Rep . 2024 Jul; 14(1):17723. PMID: 39085306
Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we...
4.
Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study
Seo H, Ahn I, Gwon H, Kang H, Kim Y, Choi H, et al.
JMIR Med Inform . 2024 Mar; 12:e53400. PMID: 38513229
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...
5.
Are polypharmacy side effects predicted by public data still valid in real-world data?
Kee G, Kang H, Ahn I, Gwon H, Kim Y, Seo H, et al.
Heliyon . 2024 Feb; 10(2):e24620. PMID: 38304832
Background And Objective: 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...
6.
Correction to: Prediction of hospitalization and waiting time within 24 h of emergency department patients with unstructured text data
Seo H, Ahn I, Gwon H, Kang H, Kim Y, Cho H, et al.
Health Care Manag Sci . 2024 Jan; 27(1):130. PMID: 38198109
No abstract available.
7.
Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea
Choi H, Kang H, Ahn I, Gwon H, Kim Y, Seo H, et al.
Sci Rep . 2023 Dec; 13(1):22461. PMID: 38105280
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(ADE) resulting from warfarin overdose can...
8.
LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis
Gwon H, Ahn I, Kim Y, Kang H, Seo H, Choi H, et al.
Comput Biol Med . 2023 Nov; 168:107738. PMID: 37995536
Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult...
9.
Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data
Seo H, Ahn I, Gwon H, Kang H, Kim Y, Cho H, et al.
Health Care Manag Sci . 2023 Nov; 27(1):114-129. PMID: 37921927
Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports...
10.
Heterogeneous graph construction and HinSAGE learning from electronic medical records
Cho H, Ahn I, Gwon H, Kang H, Kim Y, Seo H, et al.
Sci Rep . 2022 Dec; 12(1):21152. PMID: 36477457
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...