[HTML][HTML] Adverse drug event detection using natural language processing: A scoping review of supervised learning methods
To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
Machine-learning-based adverse drug event prediction from observational health data: A review
Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …
and fatalities. Machine learning models have been developed to assess individual patient …
[HTML][HTML] RobIn: A robust interpretable deep network for schizophrenia diagnosis
Schizophrenia is a severe mental health condition that requires a long and complicated
diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning …
diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning …
[HTML][HTML] Systematic mapping study of AI/machine learning in healthcare and future directions
This study attempts to categorise research conducted in the area of: use of machine learning
in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed …
in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed …
A compact review of progress and prospects of deep learning in drug discovery
H Li, L Zou, JAH Kowah, D He, Z Liu, X Ding… - Journal of Molecular …, 2023 - Springer
Background Drug discovery processes, such as new drug development, drug synergy, and
drug repurposing, consume significant yearly resources. Computer-aided drug discovery …
drug repurposing, consume significant yearly resources. Computer-aided drug discovery …
Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms
The accurate forecasting of harmful algal blooms (HABs) is hindered due to insufficient
monitoring frequency, numerous interacting factors, and large temporal variability in …
monitoring frequency, numerous interacting factors, and large temporal variability in …
A Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare
Artificial intelligence technologies such as machine learning and deep learning employ
techniques to anticipate results more effectively without human involvement. Since AI …
techniques to anticipate results more effectively without human involvement. Since AI …
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
S Modi, KA Kasmiran, NM Sharef… - Journal of Biomedical …, 2024 - Elsevier
Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …
Counterfactual explanations for survival prediction of cardiovascular ICU patients
In recent years, machine learning methods have been rapidly implemented in the medical
domain. However, current state-of-the-art methods usually produce opaque, black-box …
domain. However, current state-of-the-art methods usually produce opaque, black-box …
CATNet: Cross-event attention-based time-aware network for medical event prediction
Medical event prediction (MEP) is a fundamental task in the healthcare domain, which
needs to predict medical events, including medications, diagnosis codes, laboratory tests …
needs to predict medical events, including medications, diagnosis codes, laboratory tests …