Classification of rare diseases: a worldwide effort to contribute to the International Classification of Diseases
نویسندگان
چکیده
Most rare diseases are absent in the International Classification of Diseases (ICD10) and those with a specific code are often misclassified. As a consequence, morbidity and mortality due to rare diseases is invisible in health information systems. To overcome this difficulty, Orphanet (http://www.orpha.net) has established a partnership with WHO to ensure a fair representation of rare diseases in general. Orphanet has collected all published expert classifications and established a database of phenotypes indexed with ICD10 codes, MIM codes, genes, mode of inheritance, age of onset and class of prevalence. Phenotypes are assigned to as many classification systems as necessary to represent them. The Orphanet nomenclature of rare diseases is a stable one, directly exploitable by information systems and available on request. A WHO Topic Advisory Group on rare diseases has been established to manage the revision process. The first revised chapters currently circulating among experts and expert groups for review are Haematology, Endocrinology, Nutrition, Metabolism, Immunology, Neurology and Malformations. Revised chapters follow a primarily clinical approach, only secondarily an aetiological one up to the gene level. When several possible names are available for a disease, descriptive names formed in accordance with a clinical approach are preferred. Every entity is assigned a unique identification number. Rare diseases affecting several body systems are included in every relevant chapter, as ICD11 will be poly-axial, but a main code is proposed to allow for linearisation, according to the most severe involvement and/ or the specialist most likely to be relied on for the management of the disease. The rare disease community is invited to take an active part as the results will condition the visibility of all activities in the field. All the revised chapters open for comments are available on http://www.eucerd.eu
منابع مشابه
Epidemiology of transport accidents based on international statistical classification of diseases (ICD-10) in Mashhad, Iran
Background: Road accidents are one of the main causes of death worldwide and the second cause of death in Iran. This study aimed to investigate the epidemiology of transport accidents based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10) in patients who referred to trauma department of academic hospitals affiliated with Mashhad University of Medi...
متن کاملAnalysis of Pre-processing and Post-processing Methods and Using Data Mining to Diagnose Heart Diseases
Today, a great deal of data is generated in the medical field. Acquiring useful knowledge from this raw data requires data processing and detection of meaningful patterns and this objective can be achieved through data mining. Using data mining to diagnose and prognose heart diseases has become one of the areas of interest for researchers in recent years. In this study, the literature on the ap...
متن کاملمقایسه طبقه بندی بیماریهای قلب و عروق در برخی از کشورهای توسعه یافته با ایران
Background and Aim: Today, cardiovascular diseases are the most important public health challenges in the world. Prevention and management of cardiovascular diseases necessitate the existence of a comprehensive system for recording data. Patient medical document is one of the most important data which should be classified so to facilitate and expedite the treatment process. A comparison of card...
متن کاملGeographical Epidemiology of Cardiovascular Diseases in Zanjan Province: Analysis of Groups from the International Classification of Diseases, 10th Revision
Background and Objective: Cardiovascular diseases (CVDs) are the most common chronic diseases and the leading cause of death in the world and in Iran. The aim of this study was to explore the geographical epidemiology of CVDs in the province of Zanjan, Iran. Materials and Methods: This cross-sectional study was carried out at Zanjan University of Medical Sciences. Patients with adischarge dia...
متن کاملA Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)
Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...
متن کاملBackground: Today, due to changes in human lifestyle and consumer demand incidence of foodborne diseases is on the rise. The aim of this study was to investigate the mortality rate of foodborne diseases and their related factors. Methods: In this cross-sectional study, data on deaths registered according to the International Classification of Diseases (ICD-10th revision) were extracted from de...
متن کامل