[HTML][HTML] De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1
Abstract The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks.
Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This …
Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This …
Survey on RNN and CRF models for de-identification of medical free text
The increasing reliance on electronic health record (EHR) in areas such as medical
research should be addressed by using ample safeguards for patient privacy. These records …
research should be addressed by using ample safeguards for patient privacy. These records …
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
Background De-identification is a critical technology to facilitate the use of unstructured
clinical text while protecting patient privacy and confidentiality. The clinical natural language …
clinical text while protecting patient privacy and confidentiality. The clinical natural language …
Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation
The occurrence of crop pests and diseases always affects the development of agriculture
seriously, while pest meteorology showed that climate is important in affecting the …
seriously, while pest meteorology showed that climate is important in affecting the …
Collecting indicators of compromise from unstructured text of cybersecurity articles using neural-based sequence labelling
Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating
system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an …
system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an …
Privacy-preservation in the context of natural language processing
Data privacy is one of the highly discussed issues in recent years as we encounter data
breaches and privacy scandals often. This raises a lot of concerns about the ways the data is …
breaches and privacy scandals often. This raises a lot of concerns about the ways the data is …
Automatic identification of indicators of compromise using neural-based sequence labelling
S Zhou, Z Long, L Tan, H Guo - arXiv preprint arXiv:1810.10156, 2018 - arxiv.org
Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating
system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an …
system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an …
Fully automated machine learning system which generates and optimizes solutions given a dataset and a desired outcome
A Walters, J Goodsitt, A Truong, FAT Abad… - US Patent …, 2020 - Google Patents
Automated systems and methods for optimizing a model are disclosed. For example, in an
embodiment, a method for optimizing a model may comprise receiving a data input that …
embodiment, a method for optimizing a model may comprise receiving a data input that …
De-identification of clinical free text using natural language processing: A systematic review of current approaches
Abstract Background Electronic health records (EHRs) are a valuable resource for data-
driven medical research. However, the presence of protected health information (PHI) …
driven medical research. However, the presence of protected health information (PHI) …
Systems and methods for detecting data drift for data used in machine learning models
A Walters, J Goodsitt, A Truong, FAT Abad… - US Patent …, 2020 - Google Patents
(57) ABSTRACT A system and method for detecting data drift is disclosed. The system may
be configured to perform a method, the method including receiving model training data and …
be configured to perform a method, the method including receiving model training data and …