Machine learning for detection and diagnosis of disease.

By Paul Sajda. 2006

399 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, neutral): “In the medical diagnosis contract, we trained a model predicting the likelihood of heart disease based on medical records [75].”
Leveraging uncertainty information from deep neural networks for disease detection (2016, background): “Bayesian approaches to uncertainty estimation have indeed been proposed to assess the reliability of clinical predictions 18–21 but have not been applied to the large-scale real-world problems that DNNs can target.”
The Ekiden Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2020, methodology): “13 In both of these, we used a version of the rusty-machine 22 machine learning library, which we ported to run inside our contracts.”
Artificial Intelligence in Healthcare: Review and Prediction Case Studies (2020, positive): “AI allows health professionals to give earlier and more accurate diagnostics for many kinds of diseases [71].”
Machine Learning and Radiology (2012, background): “CADe and CADx are young interdisciplinary technologies combining elements of digital image processing, machine learning, pattern recognition, and domain knowledge of medicine together (Doi, 2005, 2007; Kononenko, 2001; Sajda, 2006).”
Applications of Artificial Intelligence in Machine Learning: Review and Prospect (2015, neutral): “[35] [50]”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “Sajda [107] reported that the Bayesian decision theory may also be applicable to other NN models.”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical decision making [1–4], prognosis [5–8], and patient management [9–12].”
eDoctor: machine learning and the future of medicine (2018, methodology): “Methods such as logistic regression, principal component analysis and latent class analysis have been used for many years; however, it is the integration with iterative supervised and unsupervised learning techniques that novel applications have emerged [13, 14, 22, 23].”
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models (2020, background): “…availability of large-scale annotated datasets [32, 12, 11], have enabled these models to match and sometimes outperform human experts on a number of tasks, including natural image classiﬁcation [33], objection recognition [21], disease diagnosis [46], and autonomous driving [8], among others.”
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations (2019, background): “, to support our argument. 1. Introduction In the past few decades, machine learning algorithms have become increasingly signiﬁcant for human decisionmaking in many areas such as automatic diagnosis (Sajda, 2006) and opinion mining (Pak & Paroubek, 2010) etc. However, many machine learning algor”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, neutral): “, 2008) with high sensitivity and specificity in individuals (Sajda, 2006) that can summarize the imaging profile of a subject into a single meaningful value (Habes et al.”