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Filename: drivers/Session_files_driver.php
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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Backtrace:
File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
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Filename: models/Detail_model.php
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Function: strpos
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Function: insertAPISummary
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Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: str_replace
File: /var/www/html/application/controllers/Detail.php
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Function: formatAIDetailSummary
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Function: require_once
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Filename: controllers/Detail.php
Line Number: 256
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
Line Number: 256
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "usage"
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 257
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 257
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
Line Number: 258
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: file_get_contents
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
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Function: pubMedGetRelatedKeyword
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File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
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Function: require_once
Background And Objectives: There is an increasing and renewed interest in Electronic Health Records (EHRs) as a substantial information source for clinical decision making. Consequently, automatic de-identification of EHRs is an indispensable task, since their dissociation from personal data is a necessary prerequisite for their dissemination. Nevertheless, the bulk of prior research in this domain has been conducted using English EHRs, given the limited availability of annotated corpora in other languages, including Spanish.
Methods: In this study, the automatic de-identification of medical documents in Spanish was explored. A private corpus comprising 599 genuine clinical cases was annotated with eight different categories of protected health information. The prediction problem was approached as a named entity recognition task and two deep learning-based methodologies were developed. The first strategy was based on recurrent neural networks (RNN) and the second, an end-to-end approach, was based on Transformers. In addition, we have implemented a procedure to expand the amount of texts employed for model training.
Results: Our findings demonstrate that Transformers surpass RNNs in the de-identification of clinical data in Spanish. Particularly noteworthy is the excellent performance of the XLM-RoBERTa large Transformer, achieving a rigorous strict-match micro-average of 0.946 for precision, 0.954 for recall, and an F1 score of 0.95 when applied to the amplified version of the corpus. Furthermore, a web-based application has been created to assist specialized clinicians in de-identifying EHRs through the aid of the implemented models.
Conclusion: The study's conclusions showcase the practical applicability of the state-of-the-art Transformers models for precise de-identification of clinical notes in real-world medical settings in Spanish, with the potential to improve performance if continual pre-training strategies are implemented.
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Source |
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http://dx.doi.org/10.1016/j.compbiomed.2024.109576 | DOI Listing |
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