Machine learning for medical diagnosis: history, state of the art and perspective

By Igor Kononenko. 2001

1,627 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Complementary and alternative medicine, Health Information Management, Medical & Health AI

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Citations in the corpus

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “…learning applications with great beneﬁts are enabled only through the analysis of sensitive data, such as users’ personal contacts, private photographs or correspondence, or even medical records or genetic sequences (Alipanahi et al., 2015; Kannan et al., 2016; Kononenko, 2001; Sweeney, 1997).”
Tempered Sigmoid Activations for Deep Learning with Differential Privacy (2020, background): “Machine learning (ML) can be usefully applied to the analysis of sensitive data, e.g., in the domain of healthcare (Kononenko 2001).”
Federated Tensor Factorization for Computational Phenotyping (2017, background): “Electronic health records (EHRs) become one of most important sources of information about patients, which provide insight into diagnoses [19] and prognoses [11], as well as assist in the development of cost-effective treatment and management programs [1, 12].”
Manipulating and Measuring Model Interpretability (2018, neutral): “ntuitions and empirically verify that interpretable models achieve their intended effects. 1 Introduction Machine learning models are used to make decisions in critical domains like medical diagnosis [39], credit risk assessment [33], judicial sentencing and bail [3, 14, 35, 37], and hiring [43]. In”
Learning Credible Models (2017, background): “• formally defining credibility in the linear setting • proposing a novel regularization term EYE (expert yielded”
Interpreting Blackbox Models via Model Extraction (2017, background): “1 INTRODUCTION Machine learning has revolutionized our ability to use data to inform critical decisions, such as medical diagnosis [8, 18, 30], bail decisions for defendants [16, 17] and the design of aircraft collision avoidance systems [27].”
A new direction to promote the implementation of artificial intelligence in natural clinical settings (2019, background): “In general, AI technologies went through three periods: expert system, traditional machine learning, and deep learning 7–9 .”
Machine Learning for Precision Medicine. (2020, neutral): “After this, new cases can be independently 2 analyzed by a physician and the machine learning model so that the results can be 3 compared to validate the model (Kononenko, 2001).”
A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, background): “large volumes of data [4] for enhanced scientific computing. The data-driven approaches, such as machine learning [5,6], have been widely applied to computational biological [7] and medical diagnosis [8], material informatics [9,10], and other predictive physics problems [4,11]. Recently, these appr”
Deep learning for denoising (2018, methodology): “, 1998) and medical diagnosis (Kononenko, 2001).”
Causality : Objectives and Assessment (2010, background): “Examples of applications of causal models include: biology, medicine and pharmacology (Oniśko et al., 1997; Herskovits and Dagher, 1997; Friedman et al., 2000; Kononenko, 2009), epidemiology (Aickin, 2002), climatology (Chu and Glymour, 2008), social and economic sciences (Kaplan, 2000; Demiralp and”
L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, background): “Electronic health records (EHRs) become one of most important sources of information about patients, which provide insight into diagnoses [19] and prognoses [11], as well as assist in the development of cost-effective treatment andmanagement programs [1, 12].”
Survey of Machine Learning Algorithms for Disease Diagnostic (2017, background): “These classifiers can be used to train nonspecialists or students to diagnose the problem [4].”