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Results for Model Interpretability

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14 papers · ranked by Valyu relevance

01
PPubMed25 Dec 2020
Explainable AI: A Review of Machine Learning Interpretability Methods
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into…
97relevance
02
PPubMed29 Jul 2019
On the interpretability of machine learning-based model for predicting hypertension
Radwa Elshawi, Mouaz H. Al-Mallah, Sherif Sakr
Background Although complex machine learning models are commonly outperforming the traditional simple interpretable models, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. The aim of this study to demonstrate the utility of various…
94relevance
03
PPubMed16 Jan 2024
A critical moment in machine learning in medicine: on reproducible and interpretable learning
Olga Ciobanu-Caraus, Anatol Aicher, Julius M. Kernbach, Luca Regli + 2 more
'Carlo Serra' 'Victor E. Staartjes'] Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp…
92relevance
04
PPubMed9 May 2024
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death
Ted L. Chang, Hongjing Xia, Sonya Mahajan, Rohit Mahajan + 5 more
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death…
92relevance
05
PPubMed6 May 2022
Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care
Laura Moss, David Corsar, Martin Shaw, Ian Piper + 1 more
'Christopher Hawthorne'] Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models…
91relevance
06
PPubMed17 Oct 2022
Interpretable machine learning methods for predictions in systems biology from omics data Sidak et al. 10.3389/fmolb.2022.926623
David Sidak, Jana Schwarzerová, Wolfram Weckwerth, Steffen Waldherr
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. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective…
91relevance
07
PPubMed28 Nov 2024
Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions Ennab and Mcheick 10.3389/frobt.2024.1444763
Mohammad Ennab, Hamid Mcheick
Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster…
90relevance
08
PPubMed28 Feb 2023
On the importance of interpretable machine learning predictions to inform clinical decision making in oncology
Sheng-Chieh Lu, Christine L. Swisher, Caroline Chung, David Jaffray + 1 more
'Chris Sidey-Gibbons'] Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient’s future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical…
90relevance
09
PPubMed6 Feb 2025
Model interpretability enhances domain generalization in the case of textual complexity modeling
Frans van der Sluis, Egon L. van den Broek
Title: Summary Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable and 166 opaque models from 77,640 tuned configurations…
89relevance
10
PPubMed11 Aug 2025
Generating and leveraging explanations of AI/ML models in materials and manufacturing research
Erick J. Braham, Jennifer M. Ruddock, James O. Hardin
Title: Summary In some technical domains, machine learning (ML) tools, typically used with large datasets, must be adapted to small datasets, opaque design spaces, and expensive data generation. Specifically, generating data in many materials or manufacturing contexts can be expensive in time, materials, and expertise.…
89relevance
11
PPubMed27 Jan 2025
Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony
Marina Dubova, Suyog Chandramouli, Gerd Gigerenzer, Peter Grünwald + 11 more
'William Holmes' 'Tania Lombrozo' 'Marco Marelli' 'Sebastian Musslick' 'Bruno Nicenboim' 'Lauren N. Ross' 'Richard Shiffrin' 'Martha White' 'Eric-Jan Wagenmakers' 'Paul-Christian Bürkner' 'Sabina J. Sloman'] The preference for simple explanations, known as the parsimony principle, has long guided the development of…
88relevance
12
PPubMed26 Jun 2022
Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare
Mohammad Ennab, Hamid Mcheick, Jae-Ho Han
The lack of interpretability in artificial intelligence models (i.e., deep learning, machine learning, and rules-based) is an obstacle to their widespread adoption in the healthcare domain. The absence of understandability and transparency frequently leads to (i) inadequate accountability and (ii) a consequent…
87relevance
13
PPubMed31 Dec 2025
Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making
Matteo Rizzo, Alberto Veneri, Matteo Marcuzzo, Alessandro Zangari + 4 more
Explainable Artificial Intelligence, or XAI, is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high-stakes decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still…
87relevance
14
PPubMed24 Apr 2023
No silver bullet: interpretable ML models must be explained
Joao Marques-Silva, Alexey Ignatiev
Recent years witnessed a number of proposals for the use of the so-called interpretable models in specific application domains. These include high-risk, but also safety-critical domains. In contrast, other works reported some pitfalls of machine learning model interpretability, in part justified by the lack of a…
80relevance

Results for Model Interpretability

Explainable AI: A Review of Machine Learning Interpretability Methods

On the interpretability of machine learning-based model for predicting hypertension

A critical moment in machine learning in medicine: on reproducible and interpretable learning

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care

Interpretable machine learning methods for predictions in systems biology from omics data Sidak et al. 10.3389/fmolb.2022.926623

Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions Ennab and Mcheick 10.3389/frobt.2024.1444763

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

Model interpretability enhances domain generalization in the case of textual complexity modeling

Generating and leveraging explanations of AI/ML models in materials and manufacturing research

Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony

Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare

Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making

No silver bullet: interpretable ML models must be explained

Explainable AI: A Review of Machine Learning Interpretability Methods

On the interpretability of machine learning-based model for predicting hypertension

A critical moment in machine learning in medicine: on reproducible and interpretable learning

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care

Interpretable machine learning methods for predictions in systems biology from omics data Sidak et al. 10.3389/fmolb.2022.926623

Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions Ennab and Mcheick 10.3389/frobt.2024.1444763

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

Model interpretability enhances domain generalization in the case of textual complexity modeling

Generating and leveraging explanations of AI/ML models in materials and manufacturing research

Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony

Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare

Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making

No silver bullet: interpretable ML models must be explained