There is paucity of data regarding prevalence and key harms of non-medical cannabis use in surgical patients. We investigated whether cannabis use in patients undergoing surgery or interventional ...procedures patients was associated with a higher degree of post-procedural healthcare utilisation.
210,639 adults undergoing non-cardiac surgery between January 2008 and June 2020 at an academic healthcare network in Massachusetts, USA, were included. The primary exposure was use of cannabis, differentiated by reported ongoing non-medical use, self-identified during structured, preoperative nursing/physician interviews, or diagnosis of cannabis use disorder based on International Classification of Diseases, 9th/10th Revision, diagnostic codes. The main outcome measure was the requirement of advanced post-procedural healthcare utilisation (unplanned intensive care unit admission, hospital re-admission or non-home discharge).
16,211 patients (7.7%) were identified as cannabis users. The prevalence of cannabis use increased from 4.9% in 2008 to 14.3% by 2020 (p < 0.001). Patients who consumed cannabis had higher rates of psychiatric comorbidities (25.3 versus 16.8%; p < 0.001) and concomitant non-tobacco substance abuse (30.2 versus 7.0%; p < 0.001). Compared to non-users, patients with a diagnosis of cannabis use disorder had higher odds of requiring advanced post-procedural healthcare utilisation after adjusting for patient characteristics, concomitant substance use and socioeconomic factors (aOR adjusted odds ratio 1.16; 95% CI 1.02–1.32). By contrast, patients with ongoing non-medical cannabis use had lower odds of advanced post-procedural healthcare utilisation (aOR 0.87; 95% CI 0.81–0.92, compared to non-users).
One in seven patients undergoing surgery or interventional procedures in 2020 reported cannabis consumption. Differential effects on post-procedural healthcare utilisation were observed between patients with non-medical cannabis use and cannabis use disorder.
This work was supported by an unrestricted philantropic grant from Jeff and Judy Buzen to Maximilian S. Schaefer.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Silicon Carbide (SiC)-based Bi-Directional Switches (BDS) have great potential in the construction of several power electronic circuits including multi-level converters, solid-state breakers, matrix ...converters, HERIC (high efficient and reliable inverter concept) photovoltaic grid-connected inverters and so on. In this paper, two issues with the application of SiC-based BDSs, namely, unwanted turn-on and parasitic oscillation, are deeply investigated. To eliminate unwanted turn-on, it is proposed to add a capacitor (CX) paralleled at the signal input port of the driver IC (integrated circuit) and the capacitance range of CX is also analytically derived to guide the selection of CX. To mitigate parasitic oscillation, a combinational method, which combines a snubber capacitor (CJ) paralleled with the JFET (Junction Field Effect Transistor) and a ferrite ring connected in series with the power line, is proposed. It is verified that the use of CJ mainly improves the turn-off transient and the use of a ferrite ring damps the current oscillation during the turn-on transient significantly. The effects of the proposed methods have been demonstrated by theoretical analysis and verified by experimental results.
Abstract Objective Near real-time disease detection using electronic data sources is a public health priority. Detecting pneumonia is particularly important because it is the manifesting disease of ...several bioterrorism agents as well as a complication of influenza, including avian and novel H1N1 strains. Text radiology reports are available earlier than physician diagnoses and so could be integral to rapid detection of pneumonia. We performed a pilot study to determine which keywords present in text radiology reports are most highly associated with pneumonia diagnosis. Design Electronic radiology text reports from 11 hospitals from February 1, 2006 through December 31, 2007 were used. We created a computerized algorithm that searched for selected keywords (“airspace disease”, “consolidation”, “density”, “infiltrate”, “opacity”, and “pneumonia”), differentiated between clinical history and radiographic findings, and accounted for negations and double negations; this algorithm was tested on a sample of 350 radiology reports. We used the algorithm to study 189,246 chest radiographs, searching for the keywords and determining their association with a final International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis of pneumonia. Measurements Performance of the search algorithm in finding keywords, and association of the keywords with a pneumonia diagnosis. Results In the sample of 350 radiographs, the search algorithm was highly successful in identifying the selected keywords (sensitivity 98.5%, specificity 100%). Analysis of the 189,246 radiographs showed that the keyword “pneumonia” was the strongest predictor of an ICD-9-CM diagnosis of pneumonia (adjusted odds ratio 11.8) while “density” was the weakest (adjusted odds ratio 1.5). In general, the most highly associated keyword present in the report, regardless of whether a less highly associated keyword was also present, was the best predictor of a diagnosis of pneumonia. Conclusion Empirical methods may assist in finding radiology report keywords that are most highly predictive of a pneumonia diagnosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper aims to propose a joint prediction model and factor contribution analysis algorithm based on KPCA-LightGBM. Firstly, kernel principal component analysis (KPCA) is used to reduce the ...dimension of nonlinear data. Secondly, light gradient boosting machine (LightGBM) is used for data regression and prediction. Finally, MSE and R^{2} were used as model accuracy tests on the predictions of the 31 time series data. The research results show that KPCA can better grasp the characteristics of nonlinear data, such as this paper simplifies 20-dimensional data to 5-dimensional, which can reflect the original data up to more than 95%, and the prediction accuracy of the joint KPCA-LightGBM model is higher compared with the traditional machine learning model, up to 94%.
Urban highway transportation is the main mode of freight transportation in China, and its proportion has steadily increased year by year. Combined with the current situation of China's social and ...industrial economic structure, taking into account the nonlinear and random characteristics of road freight volume itself, a neural network model is established. Using the actual data of all urban highway freight volumes and related influencing factors in China's 34 provinces, the input evaluation index system is determined and the test data set is divided. The evaluation system is divided into three main aspects: socio-demographic economy, urban transportation construction and urban industrial construction. By analyzing the error curve between the results and the real value, the feasibility and reliability of the neural network for predicting urban highway freight volume are verified. Finally, the experimental results are compared with the traditional OLS/panel regression prediction method. The RMSE obtained by the neural network model is 8250 and the interpretability R2 is 0.8560. The RMSE obtained by the panel regression method is as high as 13948 and the interpretability R2 is only 0.1843. The excellent comparison results further clarify the accuracy and reliability of the neural network in predicting urban highway freight volumes.
With the promotion of the national carbon neutrality policy, GS's energy consumption structure and industrial structure have changed dramatically. Timely and reliable prediction of the changes of ...regional industrial structure plays an important role in regional development planning. Industrial electricity consumption characteristics are real-time and objective feedback of production and operation status. Therefore, analyzing the correlation law of industrial electricity can obtain the evolution trend of industrial structure. This paper excavates the socio-economic value of electricity data and establishes a set of analysis framework of regional industrial structure based on industrial electricity correlation topology. Firstly, based on centrality analysis, the regional industry center is obtained from the perspective of power consumption correlation. Secondly, the trends of development in different industries are obtained by curve clustering analysis. Finally, through the topological analysis of industrial electricity, it provides a power perspective to analyze the transformation and upgrading process of regional industrial structure in different periods. The analysis results of the electricity consumption of GS's industries from 2010 to 2020 show that with the promotion and implementation of the national policy and carbon neutrality policy, most industries in GS shows an overall upward trend, and the diversified and Clustered Development Characteristics of the manufacturing industry are obvious. At the same time, on one hand, the national economy is still highly dependent on traditional high energy consuming industries. On the other hand, the elimination of highly polluting industries has achieved remarkable results. The research conclusion can provide energy assistance for the government's precise governance.
High performance analysis and processing technology of load data is an important basic of situation awareness technology in the context of power Internet of things. Under this background, a high ...performance deep learning load classification method for the massive class imbalanced load data is presented. Firstly, the mogrifier long short-term memory (Mogrifier-LSTM) network is presented as the basic classification algorithm to solve the deficiency of information interaction between input layer and hidden layer in traditional LSTM. In addition, attention mechanism and bidirectional feature fusion mechanism are introduced to further improve the ability of learning the association characteristics of time series data. Secondly, the Spark distributed computing framework is employed to realize the parallel processing of massive load data, which can improve the efficiency of classification task. And then, ensemble learning is introduced to improve the classification accuracy of the parallel processing. At last, the ADASYN method is employed to balance the load data, which can solve the potential class imbalance issue. The experiments show that the presented load classification method has advantages in terms of classification accuracy and efficiency.
Bidirectional Switches (BDSs) are basic elements in Matrix Converters (MCs). Its switching characteristics determine the performances of the converter. Changing the equivalent impedance of the BDS is ...propitious to damping the parasitic ringing during switching transient and scaling it for higher voltage applications. A Silicon Carbide Junction Field Effect Transistor (SiC JFET) BDS structure based on the cascode-light configuration is proposed in this paper. We equally present equivalent circuit model of SiC JFET BDS at turn-off and investigate the impact of snubber capacitors (\mathrm {C}_{\mathrm {S}}) paralleled across JFETs on the equivalent impedance of the BDS circuit. Theoretical analysis and experimental results of turn-off performance with and without \mathrm {C}_{\mathrm {s}} show that the proper selection of \mathrm {C}_{\mathrm {s}} is favorable to increasing the equivalent impedance of the BDS circuit and reducing the voltage overshoot at turn-off transient for high voltage applications.
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