AIMC Topic: Data Mining

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Performance of Open-Source Large Language Models to Extract Symptoms from Clinical Notes.

Studies in health technology and informatics
In this study, we examined how well the open-source foundational large language models (LLMs) can extract symptoms and signs (S&S), along with their corresponding ICD-10 codes, from clinical notes found in the public MTSamples dataset. The dataset co...

Pilot Application of a Large Language Model to Identify Hospitalisation from Unstructured Electronic Health Records in Residential Aged Care Facilities.

Studies in health technology and informatics
Older people in residential aged care facilities (RACFs) visit hospitals and utilise healthcare services more often than others in the community. Trends in hospitalization rates are essential for designing targeted aged care interventions to reduce p...

ADEPT: An advanced data exploration and processing tool for clinical data insights.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The rapid growth of clinical data creates challenges in analysis and interpretation for medical professionals. To address these issues, we developed the Advanced Data Exploration and Processing Tool (ADEPT), integrating data...

Predicting Biological Activity from Biosynthetic Gene Clusters Using Neural Networks.

Journal of chemical information and modeling
Microorganisms such as bacteria and fungi have been used for natural products that translate to drugs. However, assessing the bioactivity of extract from culture to identify novel natural molecules remains a strenuous process due to the cumbersome or...

LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.

Nucleic acids research
LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 3...

CAS: enhancing implicit constrained data augmentation with semantic enrichment for biomedical relation extraction and beyond.

Database : the journal of biological databases and curation
Biomedical relation extraction often involves datasets with implicit constraints, where structural, syntactic, or semantic rules must be strictly preserved to maintain data integrity. Traditional data augmentation techniques struggle in these scenari...

A medical information extraction model with contrastive tuning and tagging layer training.

Computers in biology and medicine
Medical information extraction, as a core task in medical intelligent systems, focuses on extracting necessary structured information from clinical texts. In recent years, deep learning-based methods have become mainstream and often achieve superior ...

Biomedical text normalization through generative modeling.

Journal of biomedical informatics
OBJECTIVE: A large proportion of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and inconsistent, making it challenging to use for predictive modeling, clinical decisi...

The dawn of a new era: can machine learning and large language models reshape QSP modeling?

Journal of pharmacokinetics and pharmacodynamics
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By mo...

Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitati...