Results 11 to 20 of about 66,762 (195)

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review

Diagnostics, 2022
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of
Yehualashet Megersa Ayano, Friedhelm Schwenker, Bisrat Derebssa Dufera, Taye Girma Debelee   +3 more
doaj   +2 more sources

Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning

Diagnostics
Background: Ventricular tachycardia (VT) can broadly be categorised into ischemic heart disease, non-ischemic structural heart disease, and idiopathic VT.
Min Wang, Zhao Hu, Ziyang Wang, Haoran Chen, Xiaowei Xu, Si Zheng, Yan Yao, Jiao Li   +7 more
doaj   +2 more sources

Interpretable discovery of semiconductors with machine learning

npj Computational Materials, 2023
Machine learning models of material properties accelerate materials discovery, reproducing density functional theory calculated results at a fraction of the cost1–6.
Hitarth Choubisa, Petar Todorović, Joao M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy, Isaac Tamblyn, Edward H. Sargent   +7 more
doaj   +2 more sources

Making machine learning models interpretable [PDF]

, 2012
Peer ...
Vellido Alcacena, Alfredo, Martin Guerrero, Jose D., Lisboa, Paulo J.G.   +2 more
openaire   +2 more sources

Techniques for interpretable machine learning [PDF]

Communications of the ACM, 2019
Uncovering the mysterious ways machine learning models make decisions.
Mengnan Du, Ninghao Liu 0001, Xia Hu 0001   +2 more
openaire   +2 more sources

Interpretable machine learning methods for predictions in systems biology from omics data

Frontiers in Molecular Biosciences, 2022
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell.
David Sidak, Jana Schwarzerová, Jana Schwarzerová, Wolfram Weckwerth, Wolfram Weckwerth, Steffen Waldherr   +5 more
doaj   +1 more source

Interpretable machine learning in Physics

CoRR, 2022
Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.
Grojean, Christophe, Paul, Ayan, Qian, Zhuoni, Strümke, Inga   +3 more
openaire   +3 more sources

Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture

Computer Methods and Programs in Biomedicine Update, 2023
Background: Machine learning (ML) has demonstrated success in classifying patients’ diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains
Tong Ling, Luo Jake, Jazzmyne Adams, Kristen Osinski, Xiaoyu Liu, David Friedland   +5 more
doaj   +1 more source

Interpretable machine learning with an ensemble of gradient boosting machines [PDF]

Knowledge-Based Systems, 2021
A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on ...
Andrei V. Konstantinov, Lev V. Utkin
openaire   +2 more sources

Interpretable Differencing of Machine Learning Models

CoRR, 2023
UAI ...
Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair 0004, Elizabeth M. Daly   +4 more
openaire   +3 more sources