All India Institute of Medical Sciences Raipur

About: All India Institute of Medical Sciences Raipur is a education organization based out in Raipur, India. It is known for research contribution in the topics: Medicine & Population. The organization has 222 authors who have published 194 publications receiving 450 citations.

Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm.

Abstract: This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). It is concluded that combining lung sound based features with spirometry data can improve the accuracy of COPD diagnosis and hence the clinician’s performance in routine clinical practice. The proposed approach is of great significance in a clinical scenario wherein it can be used to assist clinicians for automated COPD diagnosis. A complete handheld medical system can be developed in the future incorporating lung sounds for COPD diagnosis using machine learning techniques.

Covid-19 and mobile phone hygiene in healthcare settings.

Abstract: ### Summary box Hospitals and other healthcare settings can facilitate the spread of infectious diseases.1 The recent outbreak of covid-19 is the third documented spillover of animal coronavirus to humans in the past two decades, after severe acute respiratory syndrome (SARS) in 2002 and the Middle East respiratory syndrome in 2012. It has brought the focus of disease epidemiology to the healthcare institutions. The index case which sets the motion of outbreak investigations and subsequent control measures are initiated only after coming in contact with the healthcare institutions.2 Hospitals without proper infection control measures are a liability during an epidemic.3 They may become sources of hospital-acquired infections. They may initiate a vicious cycle of new disease diagnosis and newly acquired infections, both simultaneously occurring in the same hospital. For any infectious disease, it is the mode of transmission that bridges the source or reservoir with a susceptible host. It is this point which needs to be interrupted to prevent and control further disease transmission.4 Healthcare professionals are a bridge between infectious patients in hospitals (core population) and …