An investigation of interpretable deep learning for adverse drug event prediction
J Rebane, I Karlsson… - 2019 IEEE 32nd …, 2019 - ieeexplore.ieee.org
A variety of deep learning architectures have been developed for the goal of predictive
modelling in regards to detecting health diagnoses in medical records. Several models have …
modelling in regards to detecting health diagnoses in medical records. Several models have …
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
A variety of deep learning architectures have been developed for the goal of predictive
modelling and knowledge extraction from medical records. Several models have placed …
modelling and knowledge extraction from medical records. Several models have placed …
Explainable predictions of adverse drug events from electronic health records via oracle coaching
L Crielaard, P Papapetrou - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
Information about drug efficacy and safety is limited despite current research on adverse
drug events (ADEs). Electronic health records (EHRs) may be an overcoming medium …
drug events (ADEs). Electronic health records (EHRs) may be an overcoming medium …
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 …
Predictive modeling of structured electronic health records for adverse drug event detection
Background The digitization of healthcare data, resulting from the increasingly widespread
adoption of electronic health records, has greatly facilitated its analysis by computational …
adoption of electronic health records, has greatly facilitated its analysis by computational …
Assessing the clinical validity of attention-based and SHAP temporal explanations for adverse drug event predictions
Attention mechanisms form the basis of providing temporal explanations for a variety of state-
of-the-art recurrent neural network (RNN) based architectures. However, evidence is lacking …
of-the-art recurrent neural network (RNN) based architectures. However, evidence is lacking …
A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records
F Bagattini, I Karlsson, J Rebane… - BMC medical informatics …, 2019 - Springer
Abstract Background Adverse drug events (ADEs) as well as other preventable adverse
events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in …
events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in …
ADENet: a novel network-based inference method for prediction of drug adverse events
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and
improve drug safety assessment. With an increasing number of biological and medical data …
improve drug safety assessment. With an increasing number of biological and medical data …
Cascading adverse drug event detection in electronic health records
The ability to detect adverse drug events (ADEs) in electronic health records (EHRs) is
useful in many medical applications, such as alerting systems that indicate when an ADE …
useful in many medical applications, such as alerting systems that indicate when an ADE …
Machine learning-based methods and novel data models to predict adverse drug reaction
Predicting adverse drug reactions (ADRs) plays a critical role in developing new drugs and
preventing adverse reactions during the treatment of existing drugs. However, with the rapid …
preventing adverse reactions during the treatment of existing drugs. However, with the rapid …