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Rare Diseases

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Characterizing Patient Representations for Computational Phenotyping.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

Strengths and limitations of new artificial intelligence tool for rare disease epidemiology.

Journal of translational medicine
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 ...

Machine learning in rare disease.

Nature methods
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
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
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 ...

Context-Sensitive Common Data Models for Genetic Rare Diseases - A Concept.

Studies in health technology and informatics
Current challenges of rare diseases need to involve patients, physicians, and the research community to generate new insights on comprehensive patient cohorts. Interestingly, the integration of patient context has been insufficiently considered, but ...

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.

Journal of biomedical informatics
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...

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Briefings in bioinformatics
Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. ...

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the n...

DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity...