THE USE OF NEAR REAL-TIME MICROBIOGRAM FOR TREATMENT DECISION OF PATIENTS WITH PRESUMED SEPSIS

Sepsis is a severe condition caused by a series of host responses toward infection and may eventually result in death. High occurrence, mortality, and expensive treatment make it one of the most severe diseases. Despite deeper understanding of sepsis, effective therapies have saved many patients’ lives from it, yet mortality and cost are still high compared to many other diseases. Antimicrobials are effective against infections and mitigate sepsis progression if applied early and accurately. However, such treatment is hard since it needs clinicians’ expertise and a systematic understanding of the patient information. Due to the advance of data science focused on sepsis prediction, we created a near real-time microbiogram dashboard to facilitate clinicians with antimicrobial prescriptions after they have diagnosed presumed sepsis and begin therapy. It is done by displaying environmental data in Maryland and Electronic Health Record Data from Johns Hopkins Medical Institutions together to show clinicians community infection levels for different viruses. A comprehensive keyword grouping with other data management techniques is applied using Structured Query Language (SQL) and Python for data cleaning, data grouping, and filter creation. Data are visualized with Microsoft PowerBI and ESRI ArcGIS, where the information dashboard, mapping dashboard, and user interface are displayed. The microbiogram shows infection rates with precision up to census block group spatially and up to weeks temporally, allowing treatment advice at both macroscopic and microscopic levels. The filter section and reference map layers offer clinicians the freedom to customize settings and only select points of interest. Despite some trivial limitations, the microbiogram is a great starting point that combines patients’ demographics and microbiology lab tests to provide clinicians with more information about patients’ living environments. This may help clinicians to give more accurate initial antimicrobials before blood cultures results are available, and thus reduce mortality rate, reduce the cost, and improve clinical outcomes for sepsis. In addition, it’s a good platform for public health research, which can eventually benefit sepsis treatment. Therefore, it is worthy to research more on expanding the data elements, finding relationships among those, and exploring the potential to add practicability to the microbiogram

Vibration Noise Modeling for Measurement While Drilling System Based on FOGs

Aiming to improve survey accuracy of Measurement While Drilling (MWD) based on Fiber Optic Gyroscopes (FOGs) in the long period, the external aiding sources are fused into the inertial navigation by the Kalman filter (KF) method. The KF method needs to model the inertial sensors’ noise as the system noise model. The system noise is modeled as white Gaussian noise conventionally. However, because of the vibration while drilling, the noise in gyros isn’t white Gaussian noise any more. Moreover, an incorrect noise model will degrade the accuracy of KF. This paper developed a new approach for noise modeling on the basis of dynamic Allan variance (DAVAR). In contrast to conventional white noise models, the new noise model contains both the white noise and the color noise. With this new noise model, the KF for the MWD was designed. Finally, two vibration experiments have been performed. Experimental results showed that the proposed vibration noise modeling approach significantly improved the estimated accuracies of the inertial sensor drifts. Compared the navigation results based on different noise model, with the DAVAR noise model, the position error and the toolface angle error are reduced more than 90%. The velocity error is reduced more than 65%. The azimuth error is reduced more than 50%

Performance Analysis of Global Navigation Satellite System Signal Acquisition Aided by Different Grade Inertial Navigation System under Highly Dynamic Conditions

Under the high dynamic conditions, Global Navigation Satellite System (GNSS) signals produce great Doppler frequency shifts, which hinders the fast acquisition of signals. Inertial Navigation System (INS)-aided acquisition can improve the acquisition performance, whereas the accuracy of Doppler shift and code phase estimation are mainly determined by the INS precision. The relation between the INS accuracy and Doppler shift estimation error has been derived, while the relation between the INS accuracy and code phase estimation error has not been deduced. In this paper, in order to theoretically analyze the effects of INS errors on the performance of Doppler shift and code phase estimations, the connections between them are re-deduced. Moreover, the curves of the corresponding relations are given for the first time. Then, in order to have a better verification of the INS-aided acquisition, a high dynamic scenario is designed. Furthermore, by using the deduced mathematical relation, the effects of different grade INS on the GNSS (including Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)) signal acquisition are analyzed. Experimental results demonstrate that the INS-aided acquisition can reduce the search range of local frequency and code phase, and achieve fast acquisition. According to the experimental results, a suitable INS can be chosen for the deeply coupled integration

Application of Fast Dynamic Allan Variance for the Characterization of FOGs-Based Measurement While Drilling

The stability of a fiber optic gyroscope (FOG) in measurement while drilling (MWD) could vary with time because of changing temperature, high vibration, and sudden power failure. The dynamic Allan variance (DAVAR) is a sliding version of the Allan variance. It is a practical tool that could represent the non-stationary behavior of the gyroscope signal. Since the normal DAVAR takes too long to deal with long time series, a fast DAVAR algorithm has been developed to accelerate the computation speed. However, both the normal DAVAR algorithm and the fast algorithm become invalid for discontinuous time series. What is worse, the FOG-based MWD underground often keeps working for several days; the gyro data collected aboveground is not only very time-consuming, but also sometimes discontinuous in the timeline. In this article, on the basis of the fast algorithm for DAVAR, we make a further advance in the fast algorithm (improved fast DAVAR) to extend the fast DAVAR to discontinuous time series. The improved fast DAVAR and the normal DAVAR are used to responsively characterize two sets of simulation data. The simulation results show that when the length of the time series is short, the improved fast DAVAR saves 78.93% of calculation time. When the length of the time series is long ( 6 × 10 5 samples), the improved fast DAVAR reduces calculation time by 97.09%. Another set of simulation data with missing data is characterized by the improved fast DAVAR. Its simulation results prove that the improved fast DAVAR could successfully deal with discontinuous data. In the end, a vibration experiment with FOGs-based MWD has been implemented to validate the good performance of the improved fast DAVAR. The results of the experience testify that the improved fast DAVAR not only shortens computation time, but could also analyze discontinuous time series