AJR. American journal of roentgenology
Jan 29, 2025
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. The pu...
OBJECTIVES: This study aimed to evaluate the effectiveness of large language models (LLM) in assessing the methodological quality of radiomics research, using METhodological RadiomICs Score (METRICS) tool.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable f...
BACKGROUND: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are...
OBJECTIVE: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.
Journal of chemical information and modeling
Jan 28, 2025
In 2020, nearly 3 million scientific and engineering papers were published worldwide (White, K. Publications Output: U.S. Trends And International Comparisons). The vastness of the literature that already exists, the increasing rate of appearance of ...
BACKGROUND: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information a...
BMC medical informatics and decision making
Jan 28, 2025
INTRODUCTION: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021...
BACKGROUND: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting...
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