AIMC Topic: Undiagnosed Diseases

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Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.

PloS one Dec 26, 2024
Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the var...
Humans Biological Ontologies Rare Diseases Undiagnosed Diseases Phenotype Cluster Analysis
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Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

American journal of human genetics Sep 15, 2021
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is th...
Undiagnosed Diseases Rare Diseases Phenotype Multifactorial Inheritance Genomics Databases, Genetic Humans Disease Machine Learning
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Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

American heart journal May 24, 2021
BACKGROUND: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.
Diagnosis, Computer-Assisted Remote Sensing Technology Adult Male Monitoring, Ambulatory Nervous System Diseases Patient Selection Atrial Fibrillation Humans Artificial Intelligence Algorithms Female Outcome and Process Assessment, Health Care Undiagnosed Diseases
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Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

JAMA network open Feb 1, 2021
IMPORTANCE: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators ...
Rare Diseases Undiagnosed Diseases Referral and Consultation Patient Admission Young Adult Adult Male Middle Aged Infant, Newborn Reproducibility of Results Prospective Studies ROC Curve Retrospective Studies Computer Simulation Triage Female Humans Child, Preschool Whole Genome Sequencing Infant Machine Learning Child Adolescent Area Under Curve Patient Selection
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Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Journal of medical economics Jan 10, 2020
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effec...
United Kingdom Mass Screening Algorithms Undiagnosed Diseases Risk Assessment Atrial Fibrillation Markov Chains Decision Trees Humans Quality-Adjusted Life Years Cost-Benefit Analysis Machine Learning
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