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The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Pro- cessing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is annotated with PHI information. Different processing and text augmentation techniques are evaluated, along with their impact in the final performance of the model. The augmentation techniques, such as injection and generation of both Norwegian and Scandinavian Named Entities into the Swedish training corpus, showed to increase the performance in the de-identification task for both Danish and Norwegian text. This trend was also confirmed by the evaluation of model performance on a sample Norwegian gastro surgical clinical text.

With the recent advances in natural language processing and deep learning, the development of tools that can assist medical coders in ICD-10 diagnosis coding and increase their efficiency in coding discharge summaries is significantly more viable than before. To that end, one important component in the development of these models is the datasets used to train them. In this study, such datasets are presented, and it is shown that one of them can be used to develop a BERT-based language model that can consistently perform well in assigning ICD-10 codes to discharge summaries written in Swedish. Most importantly, it can be used in a coding support setup where a tool can recommend potential codes to the coders. This reduces the range of potential codes to consider and, in turn, reduces the workload of the coder. Moreover, the de-identified and pseudonymised dataset is open to use for academic users.

Clinical data, in the form of electronic health records, are rich resources that can be tapped using natural language processing. At the same time, they contain very sensitive information that must be protected. One strategy is to remove or obscure data using automatic de-identification. However, the detection of sensitive data can yield false positives. This is especially true for tokens that are similar in form to sensitive entities, such as eponyms. These names tend to refer to medical procedures or diagnoses rather than specific persons. Previous research has shown that automatic de-identification systems often misclassify eponyms as names, leading to a loss of valuable medical information. In this study, we estimate the prevalence of eponyms in a real Swedish clinical corpus. Furthermore, we demonstrate that modern transformer-based de-identification systems are more accurate in distinguishing between names and eponyms than previous approaches.

Background: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. Objective: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. Methods: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. Results: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence-based CAC innovations to

This study discusses the methods and challenges of deidentifying and pseudonymizing Norwegian clinical text for research purposes. The results of the NorDeid tool for deidentification and pseudonymization on different types of protected health information were evaluated and discussed, as well as the extension of its functionality with regular expressions to identify specific types of sensitive information. This research used a clinical corpus of adult patients treated in a gastro-surgical department in Norway, which contains approximately nine million clinical notes. The study also highlights the challenges posed by the unique language and clinical terminology of Norway and emphasizes the importance of protecting privacy and the need for customized approaches to meet legal and research requirements.

Documentation is a regular part of contemporary healthcare practices and one such documentation task is the creation of a discharge summary, which summarizes a care episode. However, to manually write discharge summaries is a time-consuming task, and research has shown that discharge summaries are often lacking quality in various respects. To alleviate this problem, text summarization methods could be applied on text from electronic health records, such as patient notes, to automatically create a discharge summary. Previous research has been conducted on this topic on text in various languages and with various methods, but no such research has been conducted on Swedish text. In this paper, four data sets extracted from a Swedish clinical corpora were used to fine-tune four BART language models to perform the task of summarizing Swedish patient notes into a discharge summary. Out of these models, the best performing model was manually evaluated by a senior, now retired, nurse and clinical coder. The evaluation results show that the best performing model produces discharge summaries of overall low quality. This is possibly due to issues in the data extracted from the Health Bank research infrastructure, which warrants further work on this topic.

Large pre-trained language models dominate the current state-of-the-art for many natural language processing applications, including the field of clinical NLP. Several studies have found that these can be susceptible to privacy attacks that are unacceptable in the clinical domain where personally identifiable information (PII) must not be exposed. However, there is no consensus regarding how to quantify the privacy risks of different models. One prominent suggestion is to quantify these risks using membership inference attacks. In this study, we show that a state-of-the-art membership inference attack on a clinical BERT model fails to detect the privacy benefits from pseudonymizing data. This suggests that such attacks may be inadequate for evaluating token-level privacy preservation of PIIs.

Computer assisted coding (CAC) of clinical text into standardized classifications such as ICD-10 is an important challenge. For frequently used ICD-10 codes, deep learning approaches have been quite successful. For rare codes, however, the problem is still outstanding. To improve performance for rare codes, a pipeline is proposed that takes advantage of the ICD-10 code hierarchy to combine semantic capabilities of deep learning and the flexibility of fuzzy logic. The data used are discharge summaries in Swedish in the medical speciality of gastrointestinal diseases. Using our pipeline, fuzzy matching computation time is reduced and accuracy of the top 10 hits of the rare codes is also improved. While the method is promising, further work is required before the pipeline can be part of a usable prototype. Code repository: https://github.com/icd-coding/zeroshot.

A computer-assisted coding tool could alleviate the burden on medical staff to assign ICD diagnosis codes to discharge summaries by utilising deep learning models to generate recommendations. However, the opaque nature of deep learning models makes it hard for humans to trust them. In this study, the explainable AI models LIME and SHAP have been applied to the clinical language model SweDeClin-BERT to explain ICD-10 codes assigned to Swedish gastrointestinal discharge summaries. The explanations have been evaluated by eight medical experts, showing a statistically higher significant difference in explainable performance for SHAP compared to LIME.

Electronic health records contain valuable information about symptoms, diagnosis, treatment and outcomes of the treatments of individual patients. However, the records may also contain information that can reveal the identity of the patients. Removing these identifiers - the Protected Health Information (PHI) - can protect the identity of the patient. Automatic de-identification is a process which employs machine learning techniques to detect and remove PHI. However, automatic techniques are imperfect in their precision and introduce noise into the data. This study examines the impact of this noise on the utility of Swedish de-identified clinical data by using human evaluators and by training and testing BERT models. Our results indicate that de-identification does not harm the utility for clinical NLP and that human evaluators are less sensitive to noise from de-identification than expected.

Research suggests that using generic language models in specialized domains may be sub-optimal due to significant domain differences. As a result, various strategies for developing domain-specific language models have been proposed, including techniques for adapting an existing generic language model to the target domain, e.g. through various forms of vocabulary modifications and continued domain-adaptive pretraining with in-domain data. Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model. Three clinical language models for Swedish, pretrained for up to ten epochs, are fine-tuned and evaluated on several downstream tasks in the clinical domain. A comparison of the language models’ downstream performance over the training epochs is conducted. The results show that the domain-specific language models outperform a general-domain language model, although there is little difference in performance between the various clinical language models. However, compared to pretraining a pure clinical language model with only in-domain data, leveraging and adapting an existing general-domain language model requires fewer epochs of pretraining with in-domain data.