INTRODUCTION: The field of Natural Language Processing (NLP) within the life sciences has exploded in its capacity to aid the extraction and analysis of data from scientific texts in recent years through the advancement of Artificial Intelligence (AI...
Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) ...
BACKGROUND: Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges.
BACKGROUND: Phenotyping, the process of systematically identifying and classifying conditions within clinical data, is a crucial first step in any data science work involving Electronic Health Records (EHRs). Traditional approaches require extensive ...
BACKGROUND: Processing scanned documents in electronic health records (EHR) was one of the problem in hospital network information management systems (HNIMS). To overcome this difficulty, the complex interactions among natural language processing (NL...
Anaesthesia management is a critical aspect of perioperative care, directly influencing postoperative recovery, pain management, and patient outcomes. Despite advancements in anaesthesia techniques, variability in patient responses and unexpected pos...
Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practices. However, existing algorithms for CRC prediction are frequently trained on datasets that primari...
Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis and intervention landscape for Autism Spectrum Disorder (ASD). This rapid review examines these app...
OBJECTIVE: Scientific publications are essential for uncovering insights, testing new drugs, and informing healthcare policies. Evaluating the quality of these publications often involves assessing their Risk of Bias (RoB), a task traditionally perfo...
OBJECTIVE: Encoder-only transformer-based language models have shown promise in automating critical appraisal of clinical literature. However, a comprehensive evaluation of the models for classifying the methodological rigor of randomized controlled ...
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