A5090330500- Biomedical Text Mining and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Pharmacovigilance and Adverse Drug Reactions
- Imbalanced Data Classification Techniques
- Radiomics and Machine Learning in Medical Imaging
- Blood donation and transfusion practices
- Chronic Disease Management Strategies
- Robotics and Automated Systems
- Genetic and phenotypic traits in livestock
- Clinical Reasoning and Diagnostic Skills
- Artificial Intelligence in Healthcare
- Health Literacy and Information Accessibility
- Digital Accessibility for Disabilities
- Chronic Lymphocytic Leukemia Research
- Text Readability and Simplification
- Social Media in Health Education
- Tactile and Sensory Interactions
Umm al-Qura University
2014-2025
University of Manchester
2017-2024
Background Drug prescriptions are often recorded in free-text clinical narratives; making this information available a structured form is important to support many health-related tasks. Although several natural language processing (NLP) methods have been proposed extract such information, challenges remain. Objective This study evaluates the feasibility of using NLP and deep learning approaches for extracting linking drug names associated attributes identified notes presents an extensive...
The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area machine learning research. Large volumes veterinary now exist curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) VetCompass, application to these datasets already (and will continue to) improve our understanding disease patterns within medicine. In part one this two article series, we...
At the beginning of COVID-19 pandemic, UK's Scientific Committee issued extreme social distancing measures, termed 'shielding', aimed at a subpopulation deemed extremely clinically vulnerable to infection. National guidance for risk stratification was based on patients' age, comorbidities and immunosuppressive therapies, including biologics that are not captured in primary care records. This process required considerable clinician time manually review outpatient letters. Our aim develop...
In part two of this mini-series, we evaluate the range machine-learning tools now available for application to veterinary clinical text-mining. These will be vital automate extraction information from large datasets narratives curated by projects such as Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes millions records preclude reading complexities notes limit usefulness more “traditional” text-mining approaches. We discuss various machine learning...
Adaptive open learning technology provides adaptive methods of interacting with the used in learning. Since most online systems are computer-based, adapting keyboard for people special needs will be great effectiveness. This article presents an based on Morse code that enable users physical disability or functional limitations to access proposed interface and interact fully given system minimal input keys. is enhanced all required functionalities could a common environment by end user. The...
Abstract Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at highest of COVID-19 requiring shielding. This guidance was based on patients’ age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. meant rheumatologists needed manually review outpatient letters score risk. The process required considerable clinician time, with shielding decisions...
<h3>Background</h3> Efficient pandemic planning is a key for providing timely response to any developing disease outbreak. For example, at the beginning of current Coronavirus 2019 (COVID-19) pandemic, UK's Scientific Committee issued extreme social distancing measures, termed 'shielding', that were aimed subset UK population who deemed especially vulnerable infection. In April 2020 British Society Rheumatology (BSR) risk stratification guide identify patients highest COVID-19 requiring...
ABSTRACT
 ObjectivesElectronic Health Records (EHRs) contain a wealth of routinely-collected data that could potentially be used to inform clinical decisions such as the choice between competing treatment regimens. Apart from structured about diagnoses and biomarkers, these records often include unstructured free-text medication prescriptions. Disjoint toolsets exist for data, making it difficult analyse datasets comprise both data. Representing items in standardised format would enable...
Monitoring the administration of drugs and adverse drug reactions are key parts pharmacovigilance. In this paper, we explore extraction mentions drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, events) from hospital discharge summaries through deep learning that relies on various representations clinical named entity recognition. This work was officially part 2018 n2c2 shared task, use data supplied as task. We developed two architecture...
<sec> <title>BACKGROUND</title> Drug prescriptions are often recorded in free-text clinical narratives; making this information available a structured form is important to support many health-related tasks. Although several natural language processing (NLP) methods have been proposed extract such information, challenges remain. </sec> <title>OBJECTIVE</title> This study evaluates the feasibility of using NLP and deep learning approaches for extracting linking drug names associated attributes...