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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. [ABSTRACT FROM AUTHOR]

Treating patients with up-to-date medical knowledge is an ongoing goal for healthcare workers and implies efficient knowledge management at the point of care. Widely available mobile wireless technologies influence practices but a significant gap remains between technological possibilities and actual usage. The purpose of this study was to analyze residents' baseline practices in managing medical knowledge and to evaluate the use and impact of an innovative multiplatform application dedicated to anesthesiology and intensive care residents. This study took place in Rennes Teaching Hospital and comprised two distinct surveys. First, in April 2018, all residents received a ten-items online survey focusing on managing medical knowledge. Then, through a second online survey constituted of ten items, we sought to assess the use of a new multiplatform cloud-based application named "DansMaBlouse", dedicated to sharing and indexing medical knowledge, in anesthesiology and intensive care residents. Among 148 residents that answered the evaluation survey, the most sought out pieces of information in clinical setting were a phone or fax number (74%), drugs' characteristics (68%) and expert guidelines (57%). The main sources were senior staff (68%), medical databases (60%) and an Internet search engine (59%). Computers and smartphones were more frequently used than bound paper notebooks. After implementation of the multiplatform application DansMaBlouse, fifty-nine (82%) of the 72 residents that answered the evaluation survey reported using the application and 39% used it more than ten times. Among application users, 90% found it easy to use and 92% agreed that it improved point-of-care access to knowledge. Accessing appropriate medical knowledge at the point of care remains an issue for residents and can be improved by a multiplatform application combining personal and shared up-to-date resources. [ABSTRACT FROM AUTHOR]

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review. The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps. The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory. Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages. [ABSTRACT FROM AUTHOR]

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection. [ABSTRACT FROM AUTHOR]

The rapid and continuous growth of data volume and its heterogeneity has become one of the most noticeable trends in healthcare, namely in medical imaging. This evolution led to the deployment of specialized information systems supported by the DICOM standard that enables the interoperability of distinct components, including imaging modalities, repositories, and visualization workstations. However, the complexity of these ecosystems leads to challenging learning curves and makes it time-consuming to mock and apply new ideas. Dicoogle is an extensible medical imaging archive server that emerges as a tool to overcome those challenges. Its extensible architecture allows the fast development of new advanced features or extends existent ones. It is currently a fundamental enabling technology in collaborative and telehealthcare environments, including research projects, screening programs, and teleradiology services. The framework is supported by a Learning Pack that includes a description of the web programmatic interface, a software development kit, documentation, and implementation samples. This article gives an in-depth view of the Dicoogle ecosystem, state-of-the-art contributions, and community impact. It starts by presenting an overview of its architectural concept, highlights some of the most representative research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Finally, the positioning of Dicoogle in the medical imaging software field is discussed through comparison with other well-known solutions. [ABSTRACT FROM AUTHOR]