2020
DOI: 10.2196/18418
|View full text |Cite
|
Sign up to set email alerts
|

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Abstract: Background Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical re… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
4
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 13 publications
(4 citation statements)
references
References 51 publications
(45 reference statements)
0
4
0
Order By: Relevance
“…The LSTM based text model performs poorly as compared to the BoW approach text model with reduction in accuracy. This can be attributed to the fact that usually, attention mechanism don’t work very well with clinical data, as also shown by the study conducted by researchers in Korea Kim et al. (2020) .…”
Section: Discussionmentioning
confidence: 95%
“…The LSTM based text model performs poorly as compared to the BoW approach text model with reduction in accuracy. This can be attributed to the fact that usually, attention mechanism don’t work very well with clinical data, as also shown by the study conducted by researchers in Korea Kim et al. (2020) .…”
Section: Discussionmentioning
confidence: 95%
“…Notably, with regard to variable importance, CPH model exhibits a straightforward interpretation as an HR, whereas a large important variable has more influence on the transition to predict an outcome, and ML models do not provide the sign of the prediction (negative or positive effect) (45). To date, the direct comparison of CPH with ML models regarding interpretation is limited due to the lack of a common metric.…”
Section: Discussionmentioning
confidence: 99%
“…Whilst attention mechanisms are potentially able to boost the performance of a neural network, they are not without limitations. They require vast amount of data for training, are not robust when generalised in other tasks other than the one they were trained for, cannot control spurious correlations in the data and no research has been undertaken to compare different attention models’ performances ( 29 , 30 ).…”
Section: Artificial Intelligencementioning
confidence: 99%