What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics
AM Musolf, ER Holzinger, JD Malley, JE Bailey-Wilson - Human Genetics, 2022 - Springer
Genetic data have become increasingly complex within the past decade, leading
researchers to pursue increasingly complex questions, such as those involving epistatic …
researchers to pursue increasingly complex questions, such as those involving epistatic …
Interpretable machine learning for dementia: a systematic review
Introduction Machine learning research into automated dementia diagnosis is becoming
increasingly popular but so far has had limited clinical impact. A key challenge is building …
increasingly popular but so far has had limited clinical impact. A key challenge is building …
SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images
Urban functional zone mapping is essential for providing deeper insights into urban
morphology and improving urban planning. The emergence of Volunteered Geographic …
morphology and improving urban planning. The emergence of Volunteered Geographic …
Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction
BACKGROUND: Despite advancements in abdominal wall reconstruction (AWR)
techniques, hernia recurrences (HRs), surgical site occurrences (SSOs), and unplanned …
techniques, hernia recurrences (HRs), surgical site occurrences (SSOs), and unplanned …
Distance approximation to support customer selection in vehicle routing problems
F Akkerman, M Mes - Annals of operations research, 2022 - Springer
Estimating the solution value of transportation problems can be useful to assign customers
to days for multi-period vehicle routing problems, or to make customer selection decisions …
to days for multi-period vehicle routing problems, or to make customer selection decisions …
The promise of automated machine learning for the genetic analysis of complex traits
The genetic analysis of complex traits has been dominated by parametric statistical methods
due to their theoretical properties, ease of use, computational efficiency, and intuitive …
due to their theoretical properties, ease of use, computational efficiency, and intuitive …
Ultra-marginal feature importance: Learning from data with causal guarantees
Scientists frequently prioritize learning from data rather than training the best possible
model; however, research in machine learning often prioritizes the latter. Marginal …
model; however, research in machine learning often prioritizes the latter. Marginal …
Development and assessment of machine learning models for individualized risk assessment of mastectomy skin flap necrosis
Objective: To develop, validate, and evaluate ML algorithms for predicting MSFN.
Background: MSFN is a devastating complication that causes significant distress to patients …
Background: MSFN is a devastating complication that causes significant distress to patients …
Deep learning on graphs for multi-omics classification of COPD
Network approaches have successfully been used to help reveal complex mechanisms of
diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite …
diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite …
[PDF][PDF] Trying to outrun causality with machine learning: Limitations of model explainability techniques for identifying predictive variables
MJ Vowels - stat, 2022 - researchgate.net
Abstract Machine Learning explainability techniques have been proposed as a means of
'explaining'or interrogating a model in order to understand why a particular decision or …
'explaining'or interrogating a model in order to understand why a particular decision or …