AIMC Topic: Natural Language Processing

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Enhancing Generalizability in Biomedical Entity Recognition: Self-Attention PCA-CLS Model.

IEEE/ACM transactions on computational biology and bioinformatics
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...

Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

Journal of vascular and interventional radiology : JVIR
To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone microwave ablation for lung tumors. In this single-center retrospective study, radio...

Coherence and comprehensibility: Large language models predict lay understanding of health-related content.

Journal of biomedical informatics
Health literacy is a prerequisite to informed health-related decision making. To facilitate understanding of information, text should be presented at an appropriate reading level for the reader. Cognitive studies suggest that the coherence of a text ...

LCDL: Classification of ICD codes based on disease label co-occurrence dependency and LongFormer with medical knowledge.

Artificial intelligence in medicine
Medical coding involves assigning codes to clinical free-text documents, specifically medical records that average over 3,000 markers, in order to track patient diagnoses and treatments. This is typically accomplished through manual assignments by he...

Self-supervised pre-trained neural network for quantum natural language processing.

Neural networks : the official journal of the International Neural Network Society
Quantum computing models have propelled advances in many application domains. However, in the field of natural language processing (NLP), quantum computing models are limited in representation capacity due to the high linearity of the underlying quan...

Augmenting Large Language Models via Vector Embeddings to Improve Domain-specific Responsiveness.

Journal of visualized experiments : JoVE
Large language models (LLMs) have emerged as a popular resource for generating information relevant to a user query. Such models are created through a resource-intensive training process utilizing an extensive, static corpus of textual data. This sta...

Contextualized race and ethnicity annotations for clinical text from MIMIC-III.

Scientific data
Observational health research often relies on accurate and complete race and ethnicity (RE) patient information, such as characterizing cohorts, assessing quality/performance metrics of hospitals and health systems, and identifying health disparities...

Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.

Journal of medical Internet research
BACKGROUND: Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk.

Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.

BMC medical informatics and decision making
BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, prese...

CALLM: Enhancing Clinical Interview Analysis Through Data Augmentation With Large Language Models.

IEEE journal of biomedical and health informatics
The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges...