AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
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...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
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...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
Ovarian cancer, a potentially life-threatening disease, is often difficult to treat. There is a critical need for innovations that can assist in improved therapy selection. Although deep learning models are showing promising results, they are employe...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in par...
Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse...
A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL)...
Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neuroscience, which uses imaging and electrophysiology modalities as primary endpoints or key dependenc...
Most biomedical information extraction (IE) approaches focus on entity types such as diseases, drugs, and genes, and relations such as gene-disease associations. In this paper, we introduce the task of methodological IE to support fine-grained qualit...