Leveraging Large Language Models for Clinical Abbreviation Disambiguation.

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods.

Unsupervised deep learning registration model for multimodal brain images.

Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-theart method at which the registration is conducted in end-to-end manner and one-shot. Therefore, a huge ground-truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)-based model based on computer tomography/magnetic resonance (CT/MR) co-registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well-experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.

An Intelligent Rule-based System for Status Epilepticus Prognostication.

BACKGROUND: Status epilepticus is one of the most common emergency neurological conditions with high morbidity and mortality. OBJECTIVE: The aim of this study is to propose an intelligent approach to determine prognosis and the most common causes and outcomes based on clinical symptoms. MATERIAL AND METHODS: In this descriptive-analytic study, a perceptron artificial neural network was used to predict the outcome of patients with status epilepticus on discharge. But this method, which is understandable, is known as black boxes. Therefore, some rules were extracted from it in this study. The case study of this paper is data of Nemazee hospital patients. RESULTS: The proposed model was prognosticated with 70% accuracy, while Bayesian network and Random Forest approaches have 51% and 46% accuracy. According to the results, recovery and mortality groups had often used phenytoin and anesthetic drugs as seizure controlling drug, respectively. Moreover, drug withdrawal and cerebral infarction were known as the most common etiology for recovery and mortality groups, respectively and there was a relationship between age and outcome, like in previous studies. CONCLUSION: To identify some factors affecting the outcome such as withdrawal, their effects either can be avoided or can use sensitive treatment for patients with poor prognosis.

On the global stability of an epidemic model of computer viruses.

In this paper, we study the global properties of a computer virus propagation model. It is, interesting to note that the classical method of Lyapunov functions combined with the Volterra-Lyapunov matrix properties, can lead to the proof of the endemic global stability of the dynamical model characterizing the spread of computer viruses over the Internet. The analysis and results presented in this paper make building blocks towards a comprehensive study and deeper understanding of the fundamental mechanism in computer virus propagation model. A numerical study of the model is also carried out to investigate the analytical results.