[HTML][HTML] Electronic health records and stratified psychiatry: bridge to precision treatment?
The use of a stratified psychiatry approach that combines electronic health records (EHR)
data with machine learning (ML) is one potentially fruitful path toward rapidly improving …
data with machine learning (ML) is one potentially fruitful path toward rapidly improving …
[HTML][HTML] Why do probabilistic clinical models fail to transport between sites
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a
computational model achieving super-human clinical performance at its training sites may …
computational model achieving super-human clinical performance at its training sites may …
Pre-large based high utility pattern mining for transaction insertions in incremental database
H Kim, C Lee, T Ryu, H Kim, S Kim, B Vo… - Knowledge-Based …, 2023 - Elsevier
High utility pattern mining has been actively researched and applied to diverse applications
because it can process the database by considering the quantity and importance of items …
because it can process the database by considering the quantity and importance of items …
[HTML][HTML] Learning the progression patterns of treatments using a probabilistic generative model
Modeling a disease or the treatment of a patient has drawn much attention in recent years
due to the vast amount of information that Electronic Health Records contain. This paper …
due to the vast amount of information that Electronic Health Records contain. This paper …
Heart failure disease prediction and stratification with temporal electronic health records data using patient representation
Accurate early prediction of heart failure and identification of heart failure sub-phenotypes
can enable in-time interventions and treatments, assist with policy decisions, and lead to a …
can enable in-time interventions and treatments, assist with policy decisions, and lead to a …
Personalized Federated Graph Learning on Non-IID Electronic Health Records
Understanding the latent disease patterns embedded in electronic health records (EHRs) is
crucial for making precise and proactive healthcare decisions. Federated graph learning …
crucial for making precise and proactive healthcare decisions. Federated graph learning …
[HTML][HTML] Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data
J Luo, L Lan, S Huang, X Zeng, Q Xiang, M Li… - Journal of Biomedical …, 2023 - Elsevier
It is extremely important to identify patients with acute pancreatitis who are at high risk for
developing persistent organ failures early in the course of the disease. Due to the irregularity …
developing persistent organ failures early in the course of the disease. Due to the irregularity …
[HTML][HTML] Multimodal risk prediction with physiological signals, medical images and clinical notes
The broad adoption of electronic health record (EHR) systems brings us a tremendous
amount of clinical data and thus provides opportunities to conduct data-based healthcare …
amount of clinical data and thus provides opportunities to conduct data-based healthcare …
Predicting Alzheimer's Disease with Interpretable Machine Learning
M Jia, Y Wu, C Xiang, Y Fang - Dementia and Geriatric Cognitive …, 2023 - karger.com
Introduction: This study aimed to develop novel machine learning models for predicting
Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We …
Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We …
Yet another icu benchmark: A flexible multi-center framework for clinical ml
R Van De Water, H Schmidt, P Elbers, P Thoral… - arXiv preprint arXiv …, 2023 - arxiv.org
Medical applications of machine learning (ML) have experienced a surge in popularity in
recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of …
recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of …