AIMC Topic: Natural Language Processing

Clear Filters Showing 3231 to 3240 of 3983 articles

Exposing Vulnerabilities in Clinical LLMs Through Data Poisoning Attacks: Case Study in Breast Cancer.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Training Large Language Models (LLMs) with billions of parameters on a dataset and publishing the model for public access is the current standard practice. Despite their transformative impact on natural language processing (NLP), public LLMs present ...

Time Matters: Examine Temporal Effects on Biomedical Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Time roots in applying language models for biomedical applications: models are trained on historical data and will be deployed for new or future data, which may vary from training data. While increasing biomedical tasks have employed state-of-the-art...

RDguru: An Intelligent Agent for Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare di...

Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Implicit bias can impede patient-provider interactions and lead to inequities in care. Raising awareness is key to reducing such bias, but its manifestations in the social dynamics of patient-provider communication are difficult to detect. In this st...

LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and...

Boosting Social Determinants of Health Extraction with Semantic Knowledge Augmented Large Language Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social determinants of health (SDoH) significantly impacts health outcomes and contributes to perpetuating health disparities across healthcare applications. However, automatic extraction of SDoH information from Electronic Health Records (EHRs) is c...

Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Patient portal messages represent a unique source of clinical data due to how they represent the voice of the patient, provide a glimpse into care delivery between episodic synchronous appointments, and capture variations in patient behavior and heal...

Extraction of Normalized Symptom Mentions From Clinical Narratives Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Symptoms, or subjective experiences of patients which can indicate underlying pathology, are important for guiding clinician decision-making and revealing patient wellbeing. However, they are difficult to study because information is primarily found ...

The Use of Large Language Models to Accelerate Literature Review Towards Digital Health Equity and Inclusiveness.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Digital health technologies (DHTs) have revolutionized clinical trials, offering unprecedented opportunities to streamline processes, enhance patient engagement, and improve data quality. Growing technology device and broadband access are contributin...