Abstract
BackgroundThe majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases.MethodsWe established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling.ResultsFour risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice.ConclusionsThis study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.
Highlights
The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors
Most of rare diseases are considered as complex diseases that are caused by a combination of multiple morbigenous factors [1]
Systematic symptoms, and multi-dimension clinical evaluations are simultaneously indispensable for the pediatric cataract prevention and treatment process [5,6,7,8,9]
Summary
The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. Uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases. Most of rare diseases are considered as complex diseases that are caused by a combination of multiple morbigenous factors [1]. Long et al BMC Ophthalmology (2017) 17:74 pathogenesis and intractable clinical situation, is a suitable test case for the exploration of computational modeling and data mining for rare diseases. Most existing modeling techniques, such as multiple regression and observed variable analyses, cannot deal with latent variables whilst SEM compensates for these issues. SEM has multiple advantages for the modeling of complex processes beyond simple correlations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
