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: 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...
OBJECTIVE: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identi...
OBJECTIVES: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.
AIMS: Structured reporting in pathology is not universally adopted and extracting elements essential to research often requires expensive and time-intensive manual curation. The accuracy and feasibility of using large language models (LLMs) to extrac...
Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potential...
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based mode...
Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mech...
BACKGROUND: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the genera...
BACKGROUND: Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods.
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