Ghada Alfattni

• Doctor of Philosophy
• Instructor at Umm al-Qura University

Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge...

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET...

Introduction At the beginning of the COVID-19 pandemic, the 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, includ...

The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives now exist curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and V...

Background Efficient pandemic planning is a key for providing a timely response to any developing disease outbreak. For example, at the beginning of the current Coronavirus disease 2019 (COVID-19) pandemic, the UK’s Scientific Committee issued extreme social distancing measures, termed ‘shielding’, that were aimed at a subset of the UK population w...

Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk 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. This meant...

Clinical Natural Language Processing (NLP) methods are increasingly used in different healthcare applications, including identification of drug exposure, disease severity progression, relation extraction, etc. However, the majority of published models use either statistical modelling or neural network based models such as LSTMs. To take advantage o...

The accurate identification of diagnoses in free clinical narratives is decisive for characterizing the patients in a medical cohort. Thefore, the knowledge extraction and information retrieval tasks must be addressed carefully. Clinical notes might present multiple qualifiers that could change the meaning of a statement: negation, speculation, tem...

Temporal relation extraction between health-related events is a widely studied task in clinical Natural Language Processing (NLP). The current state-of-the-art methods mostly rely on engineered features (i.e., rule-based modelling) and sequence modelling, which often encodes a source sentence into a single fixed-length context. An obvious disadvant...

BACKGROUND As drug prescriptions are often recorded in free-text clinical narratives, extracting such information is important to support complex health-related tasks. Several natural language processing (NLP) methods have been proposed to extract such information, but still with limited performance. OBJECTIVE This paper describes (DrugEx), which...

Background Drug prescriptions are often recorded in free-text clinical narratives; making this information available in a structured form is important to support many health-related tasks. Although several natural language processing (NLP) methods have been proposed to extract such information, many challenges remain. Objective This study evaluates...

Background Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning. Objectives To survey existing methods for extrac...

Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that r...

Objectives Electronic 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 data about diagnoses and biomarkers, these records often include unstructured data such as free-text medication prescripti...

OBJECTIVE: Electronic 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 data about diagnoses and biomarkers, these records often include unstructured data such as free-text medication prescripti...

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SM...

Adaptive open learning technology provides adaptive methods of interacting with the technology used in open learning. Since most of online learning systems are computer-based, adapting the keyboard for people with special needs will be of great effectiveness. This article presents an adaptive keyboard technology based on Morse code that will enable...