Background: Time series forecasting methods play a critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred to as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.Objective: The aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series.Comparison: The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values.Result: The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent methods of time-series prediction (ARIMA).Conclusion: The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt-Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.
Diabetes is a metabolic disorder that results from defects in autoimmune beta-cell destruction in Type 1, peripheral resistance to insulin action in Type 2 or, most commonly, both. Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. Deep learning has proven to be a success for computer-aided DR diagnosis resulting in early-detection and prevention of blindness. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Transmission Disturbance, Image Blurring and insertion of Retinal Artifacts. These degraded images were used for the training of multiple Deep Learning based Convolutional Neural Networks. We have trained InceptionV3, ResNet-50 and InceptionResNetV2 on multiple datasets. The models were used to classify retinal fundus images based on their severity level and then further used in the creation of a browser-based application, which demonstrates the model's prediction and the probability associated with each class. It will also show the Integration Gradient (IG) Attribution Mask superimposed onto the input image. The creation of the browser-based application would aid in the diagnostic procedures performed by ophthalmologists by highlighting the key features of the fundus image based on an educated prediction made by the model.
Heart rate is one of the vital signs for monitoring health. Non-invasive, non-contact assessment of heart rate can lead to safe and potentially telemedicine based monitoring. Thermal videos as a modality for capturing heart rate has been underexplored. Regions with large vessels such as the face can capture the pulsatile change in temperature associated with the blood flow. The use of a machine learning-based approach to capture heart rate from continuous thermal videos is currently lacking. Our present clinical investigation comprises the continuous monitoring of heart rate from a smaller number of samples by using a combination of an efficient deep-learning-based segmentation followed by domain-knowledge-based feature calculation for estimating heart rate from 124 thermal imaging videos comprising 3,628,087 frames of 65 patients, admitted to the pediatric intensive care unit at AIIMS, New Delhi. We hypothesized that periodic fluctuations of thermal intensity over the face can capture heart rate. Frequency domain features for thermal time series were extracted followed by supervised learning using a battery of models. A random forest model yielded the best results with a root mean squared error of 24.54 and mean absolute percentage error of 16.129. Clinical profiling of the model showed a wide range of clinical conditions in the admitted children with acceptable model performance. Affordable and commercially available thermal cameras establish the feasibility and cost viability of exploring deployments for patient heart rate estimation in non-invasive and non-contact environments.
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