“…We list the following models as benchmark models for the TSR task: TabbyPDF [19], GraphTSR [7], DeepDeSRT [8], and CATT-Net [17]. We implement four models by finetuning the Glove [20], FastText [21], and BERT [15] in accordance with the well-known pre-trained fine-tune paradigm in order to compare the state-of-the-art NLP techniques. This is because our proposed multi-task model depends on features from both image modal and text modal.…”
Section: A Implementation Details and Experimental Resultsmentioning
The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups-Header, Attribute, and Data-based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cuttingedge approaches.
“…We list the following models as benchmark models for the TSR task: TabbyPDF [19], GraphTSR [7], DeepDeSRT [8], and CATT-Net [17]. We implement four models by finetuning the Glove [20], FastText [21], and BERT [15] in accordance with the well-known pre-trained fine-tune paradigm in order to compare the state-of-the-art NLP techniques. This is because our proposed multi-task model depends on features from both image modal and text modal.…”
Section: A Implementation Details and Experimental Resultsmentioning
The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups-Header, Attribute, and Data-based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cuttingedge approaches.
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“…The representation of tokens in the text is an essential part of many NLP tasks, including clinical NER. Traditional word embeddings, such as Global vectors for word representation GloVe [44] and Word2Vec [45] provide only one global representation for each word in the text. However, words can have different meanings depending on their context.…”
Background
Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers.
Methods
We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods.
Results
A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information.
Conclusions
This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.
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