[HTML][HTML] Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …
secondary uses, such as clinical events prediction and chronic disease management …
Time series prediction using deep learning methods in healthcare
Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …
Adadiag: Adversarial domain adaptation of diagnostic prediction with clinical event sequences
Early detection of heart failure (HF) can provide patients with the opportunity for more timely
intervention and better disease management, as well as efficient use of healthcare …
intervention and better disease management, as well as efficient use of healthcare …
Unifying domain adaptation and domain generalization for robust prediction across minority racial groups
F Khoshnevisan, M Chi - Machine Learning and Knowledge Discovery in …, 2021 - Springer
In clinical deployment, the performance of a model trained from one or more medical
systems often deteriorates on another system and such deterioration is especially evident …
systems often deteriorates on another system and such deterioration is especially evident …
Reconstructing missing ehrs using time-aware within-and cross-visit information for septic shock early prediction
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing
data. In our EHRs, for example, the missing rates can be as high as 90% for some features …
data. In our EHRs, for example, the missing rates can be as high as 90% for some features …
Improving sepsis prediction model generalization with optimal transport
Sepsis is a deadly condition affecting many patients in the hospital. There have been many
efforts to build models that predict the onset of sepsis, but these models tend to perform …
efforts to build models that predict the onset of sepsis, but these models tend to perform …
An adversarial domain separation framework for septic shock early prediction across ehr systems
F Khoshnevisan, M Chi - … Conference on Big Data (Big Data), 2020 - ieeexplore.ieee.org
Modeling patient disease progression using Electronic Health Records (EHRs) is critical to
assist clinical decision making. While most of prior work has mainly focused on developing …
assist clinical decision making. While most of prior work has mainly focused on developing …
[图书][B] A variational recurrent adversarial multi-source domain adaptation framework for septic shock early prediction across medical systems
F Khoshnevisan - 2021 - search.proquest.com
Sepsis is a leading cause of death and a major challenge in US hospitals. Septic shock, the
most severe complication of sepsis, has a mortality rate of 50%. However, as many as 80 …
most severe complication of sepsis, has a mortality rate of 50%. However, as many as 80 …
Adversarial sample enhanced domain adaptation: A case study on predictive modeling with electronic health records
With the successful adoption of machine learning on electronic health records (EHRs),
numerous computational models have been deployed to address a variety of clinical …
numerous computational models have been deployed to address a variety of clinical …
A Reinforcement Learning Approach for Predicting the Onset of Septic Shock Patients with Unfair Bias
S Zhang, Z Wang, Y Zhang, S Liu - 2024 8th International …, 2024 - ieeexplore.ieee.org
Septic shock is a state in the later stages of sepsis, with an extremely high mortality rate.
Prompt and effective intervention is essential for patients to prevent the deterioration. Many …
Prompt and effective intervention is essential for patients to prevent the deterioration. Many …