Regulating AI in medicine in the United States and Europe
KN Vokinger, U Gasser - Nature machine intelligence, 2021 - nature.com
Regulating AI in medicine in the United States and Europe | Nature Machine Intelligence Skip to
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De-identification of electronic health record using neural network
According to a recent study, around 99% of hospitals across the US now use electronic
health record systems (EHRs). One of the most common types of EHR is the unstructured …
health record systems (EHRs). One of the most common types of EHR is the unstructured …
Detecting biomedical named entities in COVID-19 texts
S Raza, B Schwartz - … on Healthcare AI and COVID-19, 2022 - proceedings.mlr.press
The application of the state-of-the-art biomedical named entity recognition task faces a few
challenges: first, these methods are trained on a fewer number of clinical entities (eg …
challenges: first, these methods are trained on a fewer number of clinical entities (eg …
De-identification of unstructured clinical texts from sequence to sequence perspective
In this work, we propose a novel problem formulation for de-identification of unstructured
clinical text. We formulate the de-identification problem as a sequence to sequence learning …
clinical text. We formulate the de-identification problem as a sequence to sequence learning …
Clinical application of detecting COVID-19 risks: a natural language processing approach
The clinical application of detecting COVID-19 factors is a challenging task. The existing
named entity recognition models are usually trained on a limited set of named entities …
named entity recognition models are usually trained on a limited set of named entities …
Privacy preserving neural networks for electronic health records de-identification
Over the last decade, significant improvements and efforts in digitizing healthcare provided
us with a sizeable collection of electronic medical records. These Electronic Health Records …
us with a sizeable collection of electronic medical records. These Electronic Health Records …
Principle-based approach for the de-identification of code-mixed electronic health records
Code-mixing is a phenomenon where at least two languages are combined in a hybrid
manner in the context of a single conversation. The use of mixed language is widespread in …
manner in the context of a single conversation. The use of mixed language is widespread in …
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages
M Gunay, B Keles, R Hizlan - arXiv preprint arXiv:2412.10918, 2024 - arxiv.org
The rise of chronic diseases and pandemics like COVID-19 has emphasized the need for
effective patient data processing while ensuring privacy through anonymization and de …
effective patient data processing while ensuring privacy through anonymization and de …
A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities
S Raza, B Schwartz - arXiv preprint arXiv:2207.00876, 2022 - arxiv.org
There are a few challenges related to the task of biomedical named entity recognition, which
are: the existing methods consider a fewer number of biomedical entities (eg, disease …
are: the existing methods consider a fewer number of biomedical entities (eg, disease …
Biomedical named entity recognition using natural language processing
Motivation: Clinical entities are a type of entity in biomedical research that can be used in a
named entity recognition (NER) task to extract biomedical information. However, even the …
named entity recognition (NER) task to extract biomedical information. However, even the …