AIMC Topic: Rare Diseases
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An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.
JMIR medical informatics
Dec 18, 2024
BACKGROUND: Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Editio...
AI-Driven Drug Discovery for Rare Diseases.
Journal of chemical information and modeling
Dec 17, 2024
Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, m...
Ontology-based integration and querying of heterogeneous rare disease data sources - POLVAS perspective.
Computers in biology and medicine
Dec 2, 2024
The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Su...
Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.
Molecular systems biology
Sep 16, 2024
Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we r...
Artificial intelligence empowering rare diseases: a bibliometric perspective over the last two decades.
Orphanet journal of rare diseases
Sep 13, 2024
OBJECTIVE: To conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of ...
DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
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...
Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.
BMC neurology
Aug 3, 2024
BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, ...
Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT.
Journal of biomedical informatics
Jul 29, 2024
Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdi...
Video-based assessments of activities of daily living: generating real-world evidence in pediatric rare diseases.
Expert review of pharmacoeconomics & outcomes research
Jun 7, 2024
INTRODUCTION: Preserving function and independence to perform activities of daily living (ADL) is critical for patients and carers to manage the burden of care and improve quality of life. In children living with rare diseases, video recording ADLs o...
Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.
Sensors (Basel, Switzerland)
Jun 3, 2024
The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to asse...
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