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Rare Diseases

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[Rare disease in the age of artificial intelligence.].

Recenti progressi in medicina
INTRODUCTION: The text examines the impact of artificial intelligence (AI) in the context of rare diseases, exploring how patients turn to AI resources for health information, especially in situations where doctor-patient communication is limited. Th...

The Human Phenotype Ontology in 2024: phenotypes around the world.

Nucleic acids research
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similar...

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.

Nucleic acids research
In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature a...

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...

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. ...

An Enhanced Classification Framework for Limited IoHT Time Series Data Using Ensemble Deep Learning and Image Encoding.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent studies have illuminated the potential of harnessing the power of Deep Learning (DL) and the Internet of Health Things (IoHT) to detect a variety of disorders, particularly among patients in the middle to later stages of the disease. The utili...

KG-Hub-building and exchanging biological knowledge graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is...

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 ...

POPDx: an automated framework for patient phenotyping across 392 246 individuals in the UK Biobank study.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: For the UK Biobank, standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recog...