Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods

Background Regional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment. Methods Patient age, gender, pumped citric acid dose value, 5% NaHCO3 solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score. Results For classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively. Conclusion The results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.

Power Losses Analysis in a Three-Phase Distribution Transformer Using Artificial Neural Networks

This research article presents the analysis of power losses in a three - phase distribution transformer, 100 kVA 22 kV-400/230V by using artificial neutral networks that can analyze the power losses in distribution transformer faster and use less variables than the original method. The measurement data were collected 100,000 sets at the transformer manufacturer factory by setting the current flow from 1% to 100% at temperature 30 °C, 35 °C, 40 °C, 45 °C, 50 °C, 55°C, 60 °C, 65 °C, 70 °C, and 75 °C to calculate the power losses in a distribution transformer. The collected data were divided 80,000 sets to use for training in order to find parameters of artificial neutral networks and 20,000 sets were used for the artificial neutral networks input in order to calculate power losses in a distribution transformer. From the power losses in the distribution transformer of artificial neutral networks testing compared with the calculated valued from the measurement. The percentage error was at the satisfactory level and can be applied to design the testing of power losses in the distribution transformer in the future.

Forward-Lane Integrity Watchdog System

Today there exist a lot of smart vehicles which can change lane on their own, using their sensors to detect the vehicles around them and using various neural or non-neural algorithms to detect the lane on the road. But these are inherently limited to well-structured road environment and struggle with unstructured road or damaged road. This paper aims to propose a new system, based on cloud and deep-learning neutral networks to process images from each region to train a neural network to be highly efficient in that particular region. We use “Collective wisdom” of people along with data analysis to improve the accuracy of the model.