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Results for machine learning interpretability

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14 papers · ranked by Valyu relevance

01
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…
89relevance
02
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…
89relevance
03
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…
89relevance
04
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…
89relevance
05
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…
88relevance
06
PPubMed3 May 2022
Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset
Chuizheng Meng, Loc Trinh, Nan Xu, James Enouen + 1 more
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at…
88relevance
07
PPubMed8 Dec 2021
Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
Walid Ben Ali, Ahmad Pesaranghader, Robert Avram, Pavel Overtchouk + 6 more
'Nils Perrin' 'Stéphane Laffite' 'Raymond Cartier' 'Reda Ibrahim' 'Thomas Modine' 'Julie G. Hussin'] Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI)…
86relevance
08
PPubMed5 Aug 2024
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, Hans-Peter Lenhof
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term…
86relevance
09
PPubMed2 Jan 2025
Predicting learning achievement using ensemble learning with result explanation Ensemble learning for predicting achievement with explanation
Tingting Tong, Zhen Li, Shahid Akbar
Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these…
86relevance
10
PPubMed21 Sep 2023
An interpretable machine learning framework for diagnosis and prognosis of COVID-19 An interpretable machine learning framework for diagnosis and prognosis of COVID-19
Yongxian Fan, Meng Liu, Guicong Sun, Shahid Akbar
Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when…
85relevance
11
PPubMed6 Sep 2022
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim + 12 more
'José Pereira Amorim' 'Katerina Yordanova' 'Mor Vered' 'Rahul Nair' 'Pedro Henriques Abreu' 'Tobias Blanke' 'Valeria Pulignano' 'John O. Prior' 'Lode Lauwaert' 'Wessel Reijers' 'Adrien Depeursinge' 'Vincent Andrearczyk' 'Henning Müller'] Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to…
85relevance
12
PPubMed3 Oct 2022
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
Anne-Marie Nussberger, Lan Luo, L. Elisa Celis, M. J. Crockett
As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate…
84relevance
13
PPubMed3 Feb 2023
Interpretable machine learning for dementia: A systematic review MARTIN et al.
Sophie A. Martin, Florence J. Townend, Frederik Barkhof, James H. Cole
3.2### Risk of bias During screening, 200 papers were excluded because they did not address model interpretability. However, ascertaining what qualifies as interpretable raises important questions about what counts as an “explanation.” For instance, many studies involved the use of feature selection methods or…
82relevance
14
PPubMed28 Dec 2022
Evaluating the Impact and Potential Impact of Machine Learning on Medical Decision Making
Lauren E. Cipriano
Medical Decision Making Authors: Lauren E. Cipriano The best predictor of future behavior is past behavior. -Attributed to many authors At the recent Society for Medical Decision Making annual meeting, keynote speaker Dr. Erich Huang advised us to “think about machine learning, algorithms we generate from data, etc. as…
75relevance

Results for machine learning interpretability

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

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

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

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

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

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer

Predicting learning achievement using ensemble learning with result explanation Ensemble learning for predicting achievement with explanation

An interpretable machine learning framework for diagnosis and prognosis of COVID-19 An interpretable machine learning framework for diagnosis and prognosis of COVID-19

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence

Interpretable machine learning for dementia: A systematic review MARTIN et al.

Evaluating the Impact and Potential Impact of Machine Learning on Medical Decision Making

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

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

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

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

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

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer

Predicting learning achievement using ensemble learning with result explanation Ensemble learning for predicting achievement with explanation

An interpretable machine learning framework for diagnosis and prognosis of COVID-19 An interpretable machine learning framework for diagnosis and prognosis of COVID-19

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence

Interpretable machine learning for dementia: A systematic review MARTIN et al.

Evaluating the Impact and Potential Impact of Machine Learning on Medical Decision Making