Results 21 to 30 of about 67,619 (257)

Interpretable machine learning for dementia: A systematic review

Alzheimer's & Dementia, 2023
AbstractIntroductionMachine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained.
Sophie A. Martin, Florence J. Townend, Frederik Barkhof, James H. Cole   +3 more
openaire   +4 more sources

Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions

ImmunoInformatics, 2023
The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially for novel epitopes, because the
Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman   +4 more
doaj   +1 more source

MITRE: inferring features from microbiota time-series data linked to host status

Genome Biology, 2019
Longitudinal studies are crucial for discovering causal relationships between the microbiome and human disease. We present MITRE, the Microbiome Interpretable Temporal Rule Engine, a supervised machine learning method for microbiome time-series analysis ...
Elijah Bogart, Richard Creswell, Georg K. Gerber   +2 more
doaj   +1 more source

Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning

BMC Medical Informatics and Decision Making, 2023
Background The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure.
Chenggong Xu, Hongxia Li, Jianping Yang, Yunzhu Peng, Hongyan Cai, Jing Zhou, Wenyi Gu, Lixing Chen   +7 more
doaj   +1 more source

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach

BMC Medical Informatics and Decision Making, 2023
Background Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them.
Xiaoquan Gao, Sabriya Alam, Pengyi Shi, Franklin Dexter, Nan Kong   +4 more
doaj   +1 more source

Efficient hardware implementation of interpretable machine learning based on deep neural network representations for sensor data processing [PDF]

Journal of Sensors and Sensor Systems
With the rising number of machine learning and deep learning applications, the demand for implementation of those algorithms near the sensors has grown rapidly to allow efficient edge computing.
J. Schauer, P. Goodarzi, A. Schütze, T. Schneider   +3 more
doaj   +1 more source

Foreign direct investment and local interpretable model-agnostic explanations: a rational framework for FDI decision making [PDF]

Journal of Economics Finance and Administrative Science
Purpose – This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.
Devesh Singh
doaj   +1 more source

Clinical Applications of Machine Learning

Annals of Surgery Open
Objective:. This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background:.
Nadayca Mateussi, PhD, Michael P. Rogers, MD, Emily A. Grimsley, MD, Meagan Read, MD, Rajavi Parikh, DO, Ricardo Pietrobon, MD, PhD, Paul C. Kuo, MD   +6 more
doaj   +1 more source

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   +1 more source

Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics

Pharmaceutics
Small-molecule drug development faces high attrition rates driven by complex pharmacokinetics and unforeseen toxicities. While deep learning offers high predictive accuracy, its opaque “black-box” nature hinders mechanistic transparency, clinical trust ...
Huan Zhang, Yangyang Wang, Jihan Wang, Hui Li   +3 more
doaj   +1 more source