Statistical Similarity of Mortality and Recovery Ratios for Covid-19 Patients based on Gender and Age

Background: Studying the behavior of patients infected with Covid-19 is an essential issue for health authorities during the global pandemic, so the aim of this study is to investigate the statistical similarity between the recovery and mortality ratios based on the patients’ age and gender. For this purpose, the well-known statistical testing method of Kolmogorov-Smirnov has been utilized to investigate the similarity of distribution functions for mortality and recovery rates for patients infected with Covid-19. Results: Data for 1015 patients resulting in death, recovery, and transfer has been collected and analyzed. The age is cross-classified by gender where the rates’ cumulative distribution functions are independently calculated and depicted for females and males. The results revealed that there is no significant difference between the distribution functions of mortality and recovery rates by gender, but there is by age. Conclusion: The research results would support the health authorities in managing the admission and discharge procedures of the Covid-19 patients where the hospitality services are traditionally provided differently by gender. Doi: 10.28991/HIJ-2021-02-04-05 Full Text: PD

Flood prediction using deep learning models

Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BILSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models

Predicting Health Care Costs Using Evidence Regression

People’s health care cost prediction is nowadays a valuable tool to improve accountability in health care. In this work, we study if an interpretable method can reach the performance of black-box methods for the problem of predicting health care costs. We present an interpretable regression method based on the Dempster-Shafer theory, using the Evidence Regression model and a discount function based on the contribution of each dimension. Optimal parameters are learned using gradient descent. The k-nearest neighbors’ algorithm was also used to speed up computations. With the transparency of the evidence regression model, it is possible to create a set of rules based on a patient’s vicinity. When making a prediction, the model gives a set of rules for such a result. We used Japanese health records from Tsuyama Chuo Hospital to test our method, which includes medical checkups, exam results, and billing information from 2016 to 2017. We compared our model to an Artificial Neural Network and Gradient Boosting method. Our results showed that our transparent model outperforms the Artificial Neural Network and Gradient Boosting with an R 2 of 0 . 44