AIMC Topic: Data Mining
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Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports.
Journal of vascular surgery
Dec 16, 2024
OBJECTIVE: Participation in the Vascular Quality Initiative (VQI) provides important resources to surgeons, but the ability to do so is often limited by time and data entry personnel. Large language models (LLMs) such as ChatGPT (OpenAI) are examples...
Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.
International journal of medical informatics
Dec 12, 2024
OBJECTIVE: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.
Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences.
Journal of medical Internet research
Dec 11, 2024
BACKGROUND: Patient experience data from social media offer patient-centered perspectives on disease, treatments, and health service delivery. Current guidelines typically rely on systematic reviews, while qualitative health studies are often seen as...
Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.
JCO clinical cancer informatics
Dec 10, 2024
PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial fo...
Generative Biomedical Event Extraction With Constrained Decoding Strategy.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning and deep le...
Relation Extraction in Biomedical Texts: A Cross-Sentence Approach.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships ...
Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scien...
Integrating K+ Entities Into Coreference Resolution on Biomedical Texts.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Biomedical Coreference Resolution focuses on identifying the coreferences in biomedical texts, which normally consists of two parts: (i) mention detection to identify textual representation of biological entities and (ii) finding their coreference li...
Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect sim...
Enhancing Generalizability in Biomedical Entity Recognition: Self-Attention PCA-CLS Model.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
One of the primary tasks in the early stages of data mining involves the identification of entities from biomedical corpora. Traditional approaches relying on robust feature engineering face challenges when learning from available (un-)annotated data...
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