Electronic health records implementation: can the European Union learn from the United States?

Toscano, F; O’Donnell, Eloise; Unruh, MA; Golinelli, Davide; Carullo, Gherardo; Messina, Gabriele; Casalino, LP

doi:10.1093/eurpub/cky212.401

Cited by 6 publications

(2 citation statements)

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“…A recent study [17] found out that about 95% of the eligible hospitals and 62% of all office-based physicians used Electronic Health Records (EHR) systems. The EHR records contain patient information, history, medication prescription, vital signs, laboratory test results etc.…”

Section: Introductionmentioning

confidence: 99%

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Devlin¹,

Chang²,

Lee³

et al. 2019

Proceedings of the 2019 Conference of the North

15,802

9,737

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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

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Section: Introductionmentioning

confidence: 99%

Untitled

Devlin¹,

Chang²,

Lee³

et al. 2019

Proceedings of the 2019 Conference of the North

15,802

9,737

View full text Add to dashboard Cite

show abstract

“…In a 2016 survey, nearly 95% of eligible hospitals in the United States use EHRs [32], with that figure expected to rise in these years. The standard EHRs contain 18 categories of critical private information about patients (e.g., name, age, and address), which must be de-identified before they are made public, as required by HIPPA [33].…”

Section: Scibert [29]mentioning

confidence: 99%

Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach

Bashir

Raza

Kocaman

et al. 2022

Viruses

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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. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.

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Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Wang

Lee

et al. 2022

IEEE Access

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Code-mixing is a phenomenon when at least two languages combined in a hybrid way in the context of a single conversation. The use of mixed language is widespread in multilingual and multicultural countries and poses significant challenges for the development of automated language processing tools. In Taiwan's electronic health record (EHR) systems, the unstructured EHR texts are usually represented in the mixing of English and Chinese languages resulting in the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applied several state-of-the-art pretrained mono-and multilingual language models and proposed to apply the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus, which was annotated with 6 main categories and 25 subcategories of PHIs. In PBA, a hierarchical principle slot schema is defined to encode knowledge of code-mixed PHIs and the defined slots were learned from the training set to assemble into principles for recognizing PHI mentions and synthesizing surrogates at the same time. A semantic disambiguation process is developed used to disambiguate ambiguous PHI categories in the de-identification process and to dynamically extend the knowledge encoded in PBA during the knowledge augmentation process. The experimental results demonstrate that the proposed method can achieve the best micro-and macro-F-scores performance in comparison with the other mono-and multilingual language models finetuned on our code-mixed corpus.

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Electronic health records implementation: can the European Union learn from the United States?

Cited by 6 publications

References 0 publications

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Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach

Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

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