Development of a Minimum Data Set (MDS) for C-Section Anesthesia Information Management System (AIMS)

Background The use of a computerized anesthesia information management system provides an opportunity to scan case records electronically for deviations from specific limits for physiologic variables. Anesthesia department policy may define such deviations as intraoperative incidents and may require anesthesiologists to report their occurrence. The actual incidence of such events is not known. Neither is the level of compliance with voluntary reporting. Methods Using automated anesthesia record-keeping with long-term storage, physiologic data were recorded every 15 s from 5,454 patients undergoing noncardiothoracic surgery. Recorded measurements of blood pressure, heart rate, arterial oxygen saturation, and temperature were electronically analyzed for deviations from defined limits. The computer system also was used by anesthesiologists to report voluntarily those deviations as intraoperative incidents. For each electronically detected incident: 1) the complete automated anesthesia record was examined by two senior anesthesiologists who, by consensus, eliminated case records with artifact or in which context suggested that the incident was not clinically relevant, and 2) the anesthesia information management system database was checked for voluntary reporting. Results In 473 automated anesthesia records, 494 incidents were found by electronic scanning of 5,454 automated anesthesia records. Sixty intraoperative incidents were eliminated, 25 due to artifact and 35 due to context. When the remaining 434 intraoperative incidents were checked for voluntary reporting, 18 (4.1%) matching voluntary reports were found. All intraoperative incidents that were reported voluntarily also were detected by electronic scanning. Based on a 10% sample, the sensitivity rate of electronic scanning was 97.2% (35/36), and the specificity rate was 98.4% (427/434). Among 413 cases with electronically detected intraoperative incidents, there were 29 deaths (7.0%), whereas there were only 79 deaths (1.6%) among 5,041 cases without incidents (chi 2 = 58.5, P < 0.001). Conclusions The use of an anesthesia information management system facilitated analysis of intraoperative physiologic data and identified certain intraoperative incidents with high sensitivity and specificity. A low level of compliance with voluntary reporting of defined intraoperative incidents was found for all anesthesiologists studied. Finally, there was a strong association between intraoperative incidents and in-hospital mortality.

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The Influencing Legal and Factors of Migrant Children’s Educational Integration Based on Convolutional Neural Network

Frontiers in Psychology ◽

10.3389/fpsyg.2021.762416 ◽

2022 ◽

Vol 12 ◽

Author(s):

Chi Zhang ◽

Gang Wang ◽

Jinfeng Zhou ◽

Zhen Chen

Keyword(s):

Information Management ◽

Classification Accuracy ◽

Management System ◽

Gradual Increase ◽

Information Management System ◽

Migrant Children ◽

Beijing City ◽

Data Set ◽

Learning Skills ◽

Educational Integration

This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network (CNN) algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set (MR), Stanford sentiment data set (SST), and news opinion data set (MPQA) are used to analyze the classification accuracy, loss value, Hamming loss (HL), precision (Pre), recall (Re), and micro-F1 (F1) of the ALGCNN model. Then, on the big data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest (15.2 ± 1.38%), the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children (p < 0.05). The average final score of local children was greatly higher than that of migrant children (p < 0.05), whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model (ALGCNN) is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration.

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