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Rare Diseases

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Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases.

European journal of human genetics : EJHG
Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevan...

Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome.

Orphanet journal of rare diseases
BACKGROUND: The growing use of Electronic Health Records (EHRs) is promoting the application of data mining in health-care. A promising use of big data in this field is to develop models to support early diagnosis and to establish natural history. Dr...

Progress, challenges and global approaches to rare diseases.

Acta paediatrica (Oslo, Norway : 1992)
Rare diseases occur globally at every stage of life. Patients, families and caregivers have many unmet medical and social needs leading to extraordinary psychosocial and economic burdens. Efforts to improve diagnostic capabilities and to develop ther...

Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Rare diseases affect between 25 and 30 million people in the United States, and understanding their epidemiology is critical to focusing research efforts. However, little is known about the prevalence of many rare diseases. Given a lack of automated ...

RDmap: a map for exploring rare diseases.

Orphanet journal of rare diseases
BACKGROUND: The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases makes the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based o...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to...

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

Journal of biomedical semantics
BACKGROUND: The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientif...

Integrating ontologies of rare diseases and radiological diagnosis.

Journal of the American Medical Informatics Association : JAMIA
PURPOSE: The author sought to integrate an ontology of rare diseases with a large ontological model of radiological diagnosis.