Explaining therapy predictions with layer-wise relevance propagation in neural networks
In typical data analysis projects in biology and healthcare, simpler predictive models, such
as regressions and decision trees, enjoy more popularity than more complex and expressive …
as regressions and decision trees, enjoy more popularity than more complex and expressive …
Agree to disagree: When deep learning models with identical architectures produce distinct explanations
Deep Learning of neural networks has progressively become more prominent in healthcare
with models reaching, or even surpassing, expert accuracy levels. However, these success …
with models reaching, or even surpassing, expert accuracy levels. However, these success …
Interpretable predictions of clinical outcomes with an attention-based recurrent neural network
The increasing accumulation of healthcare data provides researchers with ample
opportunities to build machine learning approaches for clinical decision support and to …
opportunities to build machine learning approaches for clinical decision support and to …
Chronic kidney disease survival prediction with artificial neural networks
The main objective of this work is to investigate the performance of Artificial Neural Network
(ANN) models while applying to the survivability prediction on Chronic Kidney Disease …
(ANN) models while applying to the survivability prediction on Chronic Kidney Disease …
[HTML][HTML] Removing confounding factors associated weights in deep neural networks improves the prediction accuracy for healthcare applications
The proliferation of healthcare data has brought the opportunities of applying data-driven
approaches, such as machine learning methods, to assist diagnosis. Recently, many deep …
approaches, such as machine learning methods, to assist diagnosis. Recently, many deep …
Irof: a low resource evaluation metric for explanation methods
The adoption of machine learning in health care hinges on the transparency of the used
algorithms, necessitating the need for explanation methods. However, despite a growing …
algorithms, necessitating the need for explanation methods. However, despite a growing …
Dissecting deep neural networks for better medical image classification and classification understanding
Neural networks, in the context of deep learning, show much promise in becoming an
important tool with the purpose assisting medical doctors in disease detection during patient …
important tool with the purpose assisting medical doctors in disease detection during patient …
Towards complementary explanations using deep neural networks
W Silva, K Fernandes, MJ Cardoso… - … and Interpreting Machine …, 2018 - Springer
Interpretability is a fundamental property for the acceptance of machine learning models in
highly regulated areas. Recently, deep neural networks gained the attention of the scientific …
highly regulated areas. Recently, deep neural networks gained the attention of the scientific …
Diagnosis prediction via medical context attention networks using deep generative modeling
Predicting the clinical outcome of patients from the historical electronic health records
(EHRs) is a fundamental research area in medical informatics. Although EHRs contain …
(EHRs) is a fundamental research area in medical informatics. Although EHRs contain …
Interpretable clinical prediction via attention-based neural network
Background The interpretability of results predicted by the machine learning models is vital,
especially in the critical fields like healthcare. With the increasingly adoption of electronic …
especially in the critical fields like healthcare. With the increasingly adoption of electronic …