Knowledge and awareness of malocclusion among rural population in India

Malocclusion is problem since historical age. But people’s awareness and perception about the problem varies with their geographical location and cultural background and their knowledge. The problem seems to be more acute in developing countries like India and that too in rural area. Objectives: This study is undertaken to assess the awareness about problem of malocclusion and treatment seeking behaviour among the patients of rural area attending in department of Orthodontics and Dentofacial Orthopedics of Rural Dental College, PIMS, Loni. Methods: The present cross-sectional study was carried out in the Department of Orthodontic, Rural Dental College, PIMS, Loni. A total of 120 participants who were suffering from malocclusion and came for dental treatment were the study population. Result: Most of the subjects in the study are of rural area and they don’t have knowledge about the cause of the malocclusion i.e. genetic, caries, tooth loss and diet but 65% subjects know the habits can cause the malocclusion. But on the other side these rural people (92%) areaware about aesthetic problem of malocclusion. Conclusion: Prevention is always better than cure. We can prevent the malocclusion by making the rural population aware about the causes and risk factors of the malocclusion. It is suggested that treatment of malocclusion should be instituted as early as possible for improvement of facial appearance and body image

Towards Privacy-Preserving Process Mining in Healthcare

Process mining has been successfully applied in the healthcare domain and helped to uncover various insights for improving healthcare processes. While benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible use of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of privacy of such data. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Although, until recently, data privacy issues did not get much attention in the process mining community, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data. In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We also propose a framework for privacy-preserving process mining that can support healthcare process mining analyses