Abstract
In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach.
Highlights
In recent years, the availability of textual clinical data in electronic form, known as Electronic Health Records (EHRs), from which further information can be extracted to manage various critical health situations has become increasingly important
QUALITATIVE ANALYSIS The Bidirectional Long Short-Term Memory (Bi-LSTM) + Conditional Random Field (CRF) model with the proposed stacked embedding made by FastText plus Flair works both at the sub-word level and at the character level exploiting the context: the results show that this proposed stacked embedding is effective in improving the ability to detect and classify entities
In this study, a novel Italian data set was proposed for a challenging Named Entity Recognition (NER) task, i.e. clinical de-identification
Summary
The availability of textual clinical data in electronic form, known as Electronic Health Records (EHRs), from which further information can be extracted to manage various critical health situations has become increasingly important. A PHI can be assimilated to a named entity The recognition of such entities occurs by implementing what is called NER, defined as clinical if applied on medical records in the form of unstructured text. The purpose is to be able to use the data contained in them, it is necessary to identify the PHI and replace them with valid surrogates, a process called anonymisation [8]. For this reason it is important to recognize the type to which the entity belongs, and it would be more correct to refer to Named Entity Recognition and Classification (NERC). These promising deep learning systems have been started being applied to other languages different from English
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