International audience

k-anonymity [1], [2], aims to ensure that individual data cannot be distinguished from that of at least (k−1) others in the same database, regardless of additional knowledge. However, this process involves modifying data, resulting in information loss (IL). Finding the optimal solution to minimize this loss is NP-Hard [3], [4], prompting the development of heuristics. While some of these solutions are quadratic, they are impractical for large databases, leading to proposals for distributed memory environments. In these environments, some methods utilize horizontal partitioning of the database to speed up execution time, with each processor handling multiple records simultaneously. However, as the number of processors increases, the size of subsets handled by each processor decreases, leading to increased information loss, especially as k increases. This paper addresses the problem of minimizing information loss when anonymizing large databases. We propose an approach that exploits parallelism for cluster-computing using horizontal partitioning with overlaps (i.e., where partitions have common rows) to enhance information loss for the anonymization of databases. After anonymization, anonymous subsets are aggregated to create a global anonymized database by removing duplicate records. Our proposed algorithm employs parallel hierarchical aggregation that chooses a better version of an anonymized record among the different versions where it overlaps. Experimental results show that our approach is approximately 80× faster and incurs less information loss than the centralized GkAA [5], [6] algorithm.

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patient outcome as discharge or hospitalization. This study applies Deep Learning to this end. A large-scale dataset of about 260K ED records was provided by the Amiens-Picardy University Hospital in France. In general, our approach is based on integrating structured data with unstructured textual notes recorded at the triage stage. The key idea is to apply a multi-input of mixed data for training a classification model to predict hospitalization. In a simultaneous manner, the model training utilizes the numeric features along with textual data. On one hand, a standard Multi-Layer Perceptron (MLP) model is used with the standard set of features (i.e. numeric and categorical). On the other hand, a Convolutional Neural Network (CNN) is used to operate over the textual data. The two components of learning are conducted independently in parallel. The empirical results demonstrated that the classifier could achieve a very good accuracy with ROC-AUC≈0.83. The study is conceived to contribute to the mounting efforts of applying Natural Language Processing in the healthcare domain.