Search

Reducing child mortality with quality care is the prime-most concern of all nations. Thus in current IT era, our healthcare industry needs to focus on adapting information technology in healthcare services. Barring few preliminary attempts to digitalize basic hospital administrative and clinical functions, even today in India, child health and vaccination records are still maintained as paper-based records. Also, error in manually plotting the parameters in growth charts results in missed opportunities for early detection of growth disorders in children. To address these concerns, we present India's first hospital linked, affordable automated vaccination and real-time child's growth monitoring cloud based application- Integrated Child Health Record cloud (iCHRcloud). This application is based on HL7 protocol enabling integration with hospital's HIS/EMR system. It provides Java (Enterprise Service Bus and Hibernate) based web portal for doctors and mobile application for parents, enhancing doctor-parent engagement. It leverages highchart to automate chart preparation and provides access of data via Push Notification (GCM and APNS) to parents on iOS and Android mobile platforms. iCHRcloud has also been recognized as one of the best innovative solution in three nationwide challenges, 2016 in India. iCHRcloud offers a seamless, secure (256 bit HTTPS) and sustainable solution to reduce child mortality. Detail analysis on preliminary data of 16,490 child health records highlight the diversified need of various demographic regions. Thus, primary lesson would be to implement better validation strategies to fulfill the customize requisites of entire population. This paper presents first glimpse of data and power of the analytics in policy framework. [ABSTRACT FROM AUTHOR]

This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disorders such as stroke, alzheimer, parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. These diseases are critical, life-threatening and in most of the cases may lead to other precarious human disorders. Deep learning techniques are emerging soft computing technique which has been lucratively used to unravel different real-life problems such as pattern recognition (Face, Emotion, and Speech), traffic management, drug discovery, disease diagnosis, and network intrusion detection. This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also been delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. The research implication, challenges and the future directions related to the study have also been highlighted. Eventually, the research breaches are identified and it is witnessed that there is more scope in the diagnosis of migraine, cerebral palsy and stroke using different deep learning models. Likewise, there is a potential opportunity to use and explore the performance of Restricted Boltzmann Machine, Deep Boltzmann Machine and Deep Belief Network for diagnosis of different human neuropsychiatric and neurological disorders. [ABSTRACT FROM AUTHOR]

The classification of recurrence and non recurrence of Hepato Cellular carcinoma (HCC) outcome after Radio Frequency Ablation therapy is a critical task. Multiple time series clinical liver cancer dataset is collected from different dataset and time interval. A merging algorithm is used to merge all attributes collected from different sources in multiple time periods. In order to preserve the originality of information, statistical measures of each attribute is calculated and considered them as additional attributes for accurate prediction. However the merged dataset is unbalanced, in which, the number of samples from HCC recurrence class is much smaller than from HCC non recurrence. The feature weighting scheme select optimal features and parameter of classifiers are sequentially obtained from multiple iterations which causes higher computation time. In this paper, an efficient sampling approach is proposed using Inverse Random under Sampling (IRUS) to overcome class imbalance issue. IRUS under sample the majority class which creates a number of distinct partitions with a boundary separated minority and majority class samples. Additionally an optimization approach is proposed using Artificial Plant Optimization (APO) algorithm to select optimal features and parameters of classifiers to improve the effectiveness and efficiency of classification. The optimization approach reduces the number of iteration and computation time for feature selection and parameter selection for classifiers which classify the recurrence and non recurrence of HCC. Classify patients with HCC and without HCC based on optimal features and parameters by Support Vector Machine (SVM) and Random Forest(RF) classifiers. Finally, the experimental results are conducted to prove the effectiveness of the proposed method over existing method in terms of accuracy, specificity, sensitivity and balanced accuracy. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Typically, the acquired renal ultrasound image includes a course of speckle noises. This paper primarily investigates an approach for the detection of renal calculi by processing those raw US images with the help of a meta-heuristic SVM classifier. One of the major downsides of involving Ultrasound images in medical analysis is the prevalence of Speckle Noises. An Adaptive Mean Median Filter approach has been introduced in the work to get rid of the speckle noises to the maximum extent ever in the literature. Segmentation is performed by employing conventional K-Means and GLCM features were extracted for classification using a meta-heuristic SVM classifier. The proposed methodology investigates with a Real-time Acquired Dataset of Mithra Scans, Tamilnadu, India comprises of 250 clinical Ultra-Sound Kidney Images of which 150 are having Calculi and the rest are Healthy. With the experimental results, the proposed meta-heuristic SVM classifier have performed better in noisy images while comparing with other conventional methods considered in the literature. It exhibits an Accuracy of 98.8% with a FAR rate of 1.8 for FRR as high as 3.3. The results clearly proposed that the novel AMM-PSO-SVM could be a promising technique for object or foreign body detection in a medical imaging application that uses ultrasound imaging. [ABSTRACT FROM AUTHOR]

Gait analysis is considered as the most systematic study of human motion. The analysis of gait includes visual and analytical perception of the individual, augmentation of various mechanical instrumentations for measuring movement of body, muscles activity and body mechanics. Past study focused on gait analysis of various animal locomotion and humans mainly on sports biomechanics. This paper aims to quantify the gait performance with Jaipur Knee, which is one of the most widely used prosthesis in Indian population. Gait data with Jaipur knee joint is not available till date. The proposed study targets to predict the performance of Jaipur knee joint in terms of gait symmetry with transfemoral Indian amputees. Gait symmetry may be the basis of recommendation of knee joint to prosthetic patients. This study used kinematics and kinetics parameters together to quantify the performance of Jaipur knee joint to evaluate gait symmetry. This research will be helpful for clinician to predict and further to prevent the degenerated musculoskeletal effects generally seen in unilateral transfemoral amputees. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is an eye disease, which affects the people who are all having the diabetes for more than 10 years. The ophthalmologist identifies when the dilated eye exam causes severe in any one of the following deviations in the retina: changes in blood vessels, leaking blood vessels, newly grown blood vessels, swelling of the macula, changes in the lens, and damages to the nerve tissue. It can eventually lead to vision loss. The early detection of DR prevents the cause of blindness. In this paper, we propose the retinal image segmentation and extraction of blood vessels by morphological processing, thresholding, edge detection, and adaptive histogram equalization. For the automatic diagnosis of DR from the fundus image, we also developed a network with the convolutional neural network architecture for accurately classifying its severity. By using high-end graphical processor unit (GPU), we trained this network on the publicly available dataset such as DRIVE, DIARETDB0, and DIARETDB1_v1, and the images collected from the Aravind Eye Hospital, Coimbatore, India. Our proposed CNN achieves a sensitivity of 98%, a specificity of 93%, and an accuracy of 96.9% containing a database of 854 images. [ABSTRACT FROM AUTHOR]

Of late, there has been an increase in hearing impairment cases and to provide the most advantageous solutions to them is an uphill task for audiologists. Significant difficulty faced by the audiologists is in effective programming of hearing aids to provide enhanced satisfaction to the users. The main aim of our study was to develop a software intelligent system (SIS): (i) to perform the required audiological investigations for finding the degree and type of hearing loss, and (ii) to suggest appropriate values of hearing aid parameters for enhancing the speech intelligibility and the satisfaction level among the hearing aid users. In this paper, we present a Neuro-Fuzzy based SIS to automatically predict and suggest the hearing-aid parameters such as gain values, compression ratio and threshold knee point, which are needed to be fixed for different octave frequencies of sound inputs during the hearing-aid trial. The test signals for audiological investigations are generated through the standard hardware present in a personal computer system and with the aid of a software algorithm. The proposed system was validated with 243 subjects’ data collected at the Government General Hospital, Chennai, India. The calculated sensitivity, specificity and accuracy of the proposed audiometer incorporated in the SIS were 98.6%, 96.4 and 98.2%, respectively, by comparing its interpretations with those of the ‘gold standard’ audiometers. Furthermore, 91% (221 of 243) of the hearing impaired subjects attained satisfaction in the first hearing aid trials itself with the gain values as recommended by the improved SIS. The proposed system reduced around 75% of the ‘trial and error’ time spent by audiologists for enhancing satisfactory usage of the hearing aid. Hence, the proposed SIS could be used to find the degree and type of hearing loss and to recommend hearing aid parameters to provide optimal solutions to the hearing aid users. [ABSTRACT FROM AUTHOR]