Extending Machine Learning-Based Early Sepsis Detection to Different Demographics

Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis reveals the effectiveness of these methods in addressing healthcare data imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight edge in computational efficiency and scalability. The study paves the way for the broader application of machine learning in critical care, thereby expanding the reach of predictive analytics in healthcare globally.

Ultraviolet Scattering Communication Using Subcarrier Intensity Modulation over Atmospheric Turbulence Channels

A closed-form non-line-of-sight (NLOS) turbulenceinduced fluctuation model is derived for ultraviolet scattering communication (USC), which models the received irradiance fluctuation by Meijer G function. Based on this model, we investigate the error rates of the USC system in NLOS case using different modulation techniques. Closed-form error rate results are derived by integration of Meijer G function. Inspired by the decomposition of different turbulence parameters, we use a series expansion of hypergeometric function and obtain the error rate expressions by the sum of four infinite series. The numerical results show that our error rate results are accurate in NLOS case. We also study the relationship between the turbulence influence and NLOS transceiver configurations. The numerical results show that when two-LOS link formulates the same distance, the turbulence influence is the strongest for long ranges and the weakest for short ranges.

Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases

In this paper, we propose the neural Born iteration method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iteration method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions with their corresponding residuals. Two different schemes of NeuralBIMs are presented in this paper including supervised and unsupervised learning schemes. With the data set generated by method of moments (MoM), supervised NeuralBIMs are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded loss functions founding on the governing equations of ISPs, which results in no requirements of total fields and contrasts for training. Representative numerical results further validate the effectiveness and competitiveness of both supervised and unsupervised NeuralBIMs.