AIMC Topic: Humans

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Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less ex...

Unsupervised SoftOtsuNet Augmentation for Clinical Dermatology Image Classifiers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Data Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clin...

Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Complete and accurate race and ethnicity (RE) patient information is important for many areas of biomedical informatics research, such as defining and characterizing cohorts, performing quality assessments, and identifying health inequities. Patient-...

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as t...

Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Intelligent prediction of risk of blood transfusion among hospitalized patients can identify at-risk patients and provide timely information to the hospital to plan and reserve resources to meet the demand of blood transfusion. While previously propo...

Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Breast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effective method for early detection, screening rate has remained low. Given that understanding health p...

Usability and Recall Evaluation of Virtual Reality Ontology Object Manipulation (VROOM) System.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Biomedical ontologies are repositories of knowledge that encapsulate biomedical terms and the relationships between them. When visualized, ontologies are complex graphs, where each node represents one biomedical concept, and links express binary rela...

Evaluating Deep Learning Performance for P300 Neural Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
P300 event-related potential (ERP) signals are useful neurological biomarkers, and their accurate classification is important when studying the cognitive functions in patients with neurological disorders. While many studies have proposed models for c...

Automatic Mapping of Terminology Items with Transformers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Biomedical ontologies are a key component in many systems for the analysis of textual clinical data. They are employed to organize information about a certain domain relying on a hierarchy of different classes. Each class maps a concept to items in a...

Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Translating prediction models into practice and supporting clinicians' decision-making demand demonstration of clinical value. Existing approaches to evaluating machine learning models emphasize discriminatory power, which is only a part of the medic...