Results 31 to 40 of about 66,762 (195)

Interpretable Machine Learning of Two-Photon Absorption

, 2022
Molecules with strong two-photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC ...
Yiheng, Dai, Cheng, Wang, Yuming, Su, Caiyun, Wei, Pavlo O., Dral, Da, Zhou, Yifan, Zeng, Yangtao, Chen, Fuchun, Ge, Peikun, Zheng   +9 more
core   +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 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 machine learning for real-world applications

, 2023
Recent severe failures of black box models in high stakes decisions have increased interest in interpretable machine learning. In this cumulative thesis, I discuss why black box machine learning models can fail and explain the potential of interpretable ...
Stojanović, Olivera
core   +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 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

Machine-Learning-Assisted Synthesis of Polar Racemates

, 2020
Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu­(bpy)2(H2O)]2[MF6]2·3H2O (M =
Joshua Schrier (1272144), Ian M. Pendleton (1896382), Gene M. Nolis (1801516), Jordi Cabana (1317426), Kenneth R. Poeppelmeier (1351275), Alexander J. Norquist (1687342), Kent J. Griffith (1598332), Matthew L. Nisbet (8703195)   +7 more
core   +6 more sources

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

PLoS ONE, 2023
BackgroundThere is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and ...
Ben Allen
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

An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause

Space Weather, 2023
In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD).
Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen   +2 more
doaj   +1 more source