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Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We...
The Problems of Realism-Based Ontology Design: a Case Study in Creating Definitions for an Application Ontology for Diabetes Camps.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
A requirement of realism-based ontology design is that classes denote exclusively entities that exist objectively in reality and that their definitions adhere to strict criteria to ensure that the classes are re-usable in other ontologies while prese...
Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and effic...
Mining the literature for genes associated with placenta-mediated maternal diseases.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Automated literature analysis could significantly speed up understanding of the role of the placenta and the impact of its development and functions on the health of the mother and the child. To facilitate automatic extraction of information about pl...
A Semantic Parsing Method for Mapping Clinical Questions to Logical Forms.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
This paper presents a method for converting natural language questions about structured data in the electronic health record (EHR) into logical forms. The logical forms can then subsequently be converted to EHR-dependent structured queries. The natur...
Stage-Specific Survivability Prediction Models across Different Cancer Types.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
For all cancer types, survivability rates vary widely across different stages of cancer. But survivability prediction models built in past were trained using examples of all stages together and were also evaluated on all stages together. In this work...
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelera...
Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of t...
Extracting Healthcare Quality Information from Unstructured Data.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Healthcare quality research is a fundamental task that involves assessing treatment patterns and measuring the associated patient outcomes to identify potential areas for improving healthcare. While both qualitative and quantitative approaches are us...
Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in deep learning are able to capture high-quality knowledge from textual data and represent it in graphi...
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