AI Medical Compendium Journal:
AMIA ... Annual Symposium proceedings. AMIA Symposium

Showing 61 to 70 of 377 articles

A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to ...

Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms ...

Neural Granger Causal Discovery for Derangements in ICU-Acquired Acute Kidney Injury Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Nowadays, healthcare systems increasingly utilize automated surveillance of electronic medical record (EMR) data to detect adverse events with specific patterns. Despite these technological advances, the early identification of adverse events remains...

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...

Analyzing Dementia Caregivers' Experiences on Twitter: A Term-Weighted Topic Modeling Approach.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dementia profoundly impacts patients and their families, making it essential to understand the experiences and concerns offamily caregivers for enhanced support and care. This study introduces a novel approach to analyzing tweets from individuals who...