Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with the web server for the woman and the computer server for the expert). Data came from 5249 (or 4833) cardiotocography traces in Anam Hospital for the mobile application (or the computer server). 150 data cases of 5-minute duration were extracted from each trace with 141,001 final cases for the mobile application and for the computer server alike. The dependent variable was fetal status with two categories (Normal, Abnormal) for the mobile application and three categories (Normal, Middle, Abnormal) for the computer server. The fetal heart rate served as a predictor for the mobile application and the computer server, while uterus contraction for the computer server only. The 1-dimension (or 2-dimension) Resnet CNN was trained for the mobile application (or the computer server) during 800 epochs. The sensitivity, specificity and their harmonic mean of the 1-dimension CNN for the mobile application were 94.9%, 91.2% and 93.0%, respectively. The corresponding statistics of the 2-dimension CNN for the computer server were 98.0%, 99.5% and 98.7%. The average inference time per 1000 images was 6.51 micro-seconds. Deep learning provides an efficient model for the real-time classification of fetal status in the mobile application and the computer server at the same time.
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Source |
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http://dx.doi.org/10.1007/s10916-023-01960-1 | DOI Listing |
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