AIMC Topic: Rare Diseases

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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 ...

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...

AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT.

Scientific reports
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 ...

[Artificial intelligence in the diagnosis of rare disorders: the development of phenotype analysis].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
Rare diseases can often be diagnosed by carefully assessing the phenotype of the patient, as characteristic deviations (dysmorphisms) occur in many genetic diseases. These affect, for example, the features of the face - the "facial gestalt."This pape...

Deep learning for rare disease: A scoping review.

Journal of biomedical informatics
Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of e...

OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

American journal of human genetics
Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated w...

Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.

BMC bioinformatics
BACKGROUND AND OBJECTIVE: Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners...

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nature genetics
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient pho...

Knowledge-based approaches to drug discovery for rare diseases.

Drug discovery today
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics dat...