[HTML][HTML] Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances
The importance of incorporating Natural Language Processing (NLP) methods in clinical
informatics research has been increasingly recognized over the past years, and has led to …
informatics research has been increasingly recognized over the past years, and has led to …
A systematic review of echo state networks from design to application
A recurrent neural network (RNN) has demonstrated its outstanding ability in sequence
tasks and has achieved state of the art in many applications, such as industrial and medical …
tasks and has achieved state of the art in many applications, such as industrial and medical …
Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory
Background Unplanned readmission of a hospitalized patient is an indicator of patients'
exposure to risk and an avoidable waste of medical resources. In addition to hospital …
exposure to risk and an avoidable waste of medical resources. In addition to hospital …
Intensive care unit mortality prediction: An improved patient-specific stacking ensemble model
The intensive care unit (ICU) admits the most seriously ill patients requiring extensive
monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great …
monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great …
Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications
The recent availability of electronic health records (EHRs) have provided enormous
opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has …
opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has …
Paniniqa: Enhancing patient education through interactive question answering
A patient portal allows discharged patients to access their personalized discharge
instructions in electronic health records (EHRs). However, many patients have difficulty …
instructions in electronic health records (EHRs). However, many patients have difficulty …
Improving fairness in ai models on electronic health records: The case for federated learning methods
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …
applications such as those in healthcare. However, health AI models' overall prediction …
RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing
Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers
both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However …
both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However …
Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning
Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …
[HTML][HTML] Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients
Robust and rabid mortality prediction is crucial in intensive care units because it is
considered one of the critical steps for treating patients with serious conditions. Combining …
considered one of the critical steps for treating patients with serious conditions. Combining …