28 research outputs found
Improving Attention-Based Interpretability of Text Classification Transformers
Transformers are widely used in NLP, where they consistently achieve
state-of-the-art performance. This is due to their attention-based
architecture, which allows them to model rich linguistic relations between
words. However, transformers are difficult to interpret. Being able to provide
reasoning for its decisions is an important property for a model in domains
where human lives are affected, such as hate speech detection and biomedicine.
With transformers finding wide use in these fields, the need for
interpretability techniques tailored to them arises. The effectiveness of
attention-based interpretability techniques for transformers in text
classification is studied in this work. Despite concerns about attention-based
interpretations in the literature, we show that, with proper setup, attention
may be used in such tasks with results comparable to state-of-the-art
techniques, while also being faster and friendlier to the environment. We
validate our claims with a series of experiments that employ a new feature
importance metric.Comment: 13 pages, 6 figures, 6 tables, to be submitted to conferenc
A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy
Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
In recent years, a large number of XAI (eXplainable Artificial Intelligence)
solutions have been proposed to explain existing ML (Machine Learning) models
or to create interpretable ML models. Evaluation measures have recently been
proposed and it is now possible to compare these XAI solutions. However,
selecting the most relevant XAI solution among all this diversity is still a
tedious task, especially when meeting specific needs and constraints. In this
paper, we propose AutoXAI, a framework that recommends the best XAI solution
and its hyperparameters according to specific XAI evaluation metrics while
considering the user's context (dataset, ML model, XAI needs and constraints).
It adapts approaches from context-aware recommender systems and strategies of
optimization and evaluation from AutoML (Automated Machine Learning). We apply
AutoXAI to two use cases, and show that it recommends XAI solutions adapted to
the user's needs with the best hyperparameters matching the user's constraints.Comment: 16 pages, 7 figures, to be published in CIKM202