AIMC Topic: Rare Diseases
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RDDL: A systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications.
Computational biology and chemistry
Jul 22, 2023
Identifying lowly prevalent diseases, or rare diseases, in their early stages is key to disease treatment in the medical field. Deep learning techniques now provide promising tools for this purpose. Nevertheless, the low prevalence of rare diseases e...
The Application of Artificial Intelligence in the Diagnosis of Cancer and Rare Genetic Diseases.
Genetic testing and molecular biomarkers
Jul 20, 2023
Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis.
Medical image analysis
Jun 30, 2023
Deep neural networks (DNNs) have been widely applied in the medical image community, contributing to automatic ophthalmic screening systems for some common diseases. However, the incidence of fundus diseases patterns exhibits a typical long-tailed di...
A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.
Journal of biomedical informatics
Jun 22, 2023
BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to im...
Machine learning in rare disease.
Nature methods
May 29, 2023
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types ...
Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.
Molecular systems biology
May 26, 2023
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue R...
Ontology-driven and weakly supervised rare disease identification from clinical notes.
BMC medical informatics and decision making
May 5, 2023
BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for ...
Strengths and limitations of new artificial intelligence tool for rare disease epidemiology.
Journal of translational medicine
Apr 30, 2023
The recent paper by Kariampuzha et al. describes an exciting application of artificial intelligence to rare disease epidemiology. The authors' AI model appears to offer a major leap over Orphanet, the resource which is often a "first stop" for basic ...
Characterizing Patient Representations for Computational Phenotyping.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 29, 2023
Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets o...
AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT.
Scientific reports
Mar 16, 2023
The aim of the study was to evaluate the impact of the newly developed Similar patient search (SPS) Web Service, which supports reading complex lung diseases in computed tomography (CT), on the diagnostic accuracy of residents. SPS is an image-based ...
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