A5065781374- Intracranial Aneurysms: Treatment and Complications
- Biomedical Text Mining and Ontologies
- Traumatic Brain Injury and Neurovascular Disturbances
- Cerebrovascular and Carotid Artery Diseases
- Topic Modeling
- Mental Health via Writing
- Mental Health Research Topics
- Semantic Web and Ontologies
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Web Data Mining and Analysis
- Bioinformatics and Genomic Networks
- Machine Learning in Healthcare
- Genomics and Rare Diseases
- Data Mining Algorithms and Applications
- Artificial Intelligence in Healthcare and Education
- Acute Ischemic Stroke Management
- Substance Abuse Treatment and Outcomes
- Neurosurgical Procedures and Complications
- Data Quality and Management
University of Colorado Anschutz Medical Campus
2023
Oakland University
2016-2022
Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing ontologies, semantic models, knowledge graphs translational research. App an integrated platform combining about genes, phenotypes, diseases across species. Monarch's APIs enable access to carefully...
The advent of social networking and open health web forums such as PatientsLikeMe, WebMD, ehealth forum etc. have provided avenues for user data that can prove instrumental in suggesting futuristic trends healthcare. Homophily networks is a vital contributor analyzing patterns medical conditions, diagnosis treatment options. Since, members with similar issues contribute to common discussion pool; this offers rich source information be utilized. This paper intends explore growing Mental...
Social network patient data for comorbid studies is a sparsely explored avenue. This can provide unprecedented insight into disease conditions and their progression, hence facilitating improvement of healthcare public health services. Structuring scattered social mapping with standard ontologies to build reference-able knowledge base be used in evidence-based decision support systems. In this paper, we attempt address direction application where time relationships are established between...
The growing trends in Internet usage for data and knowledge sharing calls dynamic classification of web contents, particularly at the edges Internet. Rather than considering Linked Data as an integral part Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) unstructured using metadata Delivery Network (CDN). proposed framework ensures efficient categorization URLs (even overlapping categories) by dynamically mapping changing user-defined categories to...
The advent of social networking and open health web forums such as PatientsLikeMe, WebMD, ehealth forum etc. have provided avenues for user data that can prove instrumental in suggesting futuristic trends healthcare. Homophily networks is a vital contributor analyzing patterns medical conditions, diagnosis treatment options. Since, members with similar issues contribute to common discussion pool; this offers rich source information be utilized. This paper intends explore growing Mental...
Effective data-driven biomedical discovery requires data curation: a time-consuming process of finding, organizing, distilling, integrating, interpreting, annotating, and validating diverse information into structured form suitable for databases knowledge bases. Accurate efficient curation these digital assets is critical to ensuring that they are FAIR, trustworthy, sustainable. Unfortunately, expert curators face significant time resource constraints. The rapid pace new being published...
Ruptured intracranial aneurysms are associated with a high rate of mortality and disability due to the difficulty in predicting rupture complexity condition itself. Clinical narratives such as progress summaries radiological reports, etc. contain key biomarkers, medical signs, symptoms. By applying ontology-based information extraction on clinical extract important evidences subsequently using machine learning can help make decision support tools for complex making prediction aneurysm...
<sec> <title>BACKGROUND</title> Making clinical decisions about the treatment of Intracranial Aneurysms (IA) is not straightforward: small IAs in certain arteries may rupture while larger at others do not. Since many IA’s for years risk side effects from prior to must be weighed against SAH. Existing statistical and traditional approaches neither provide an accurate prediction aneurysmal nor offer a quantitative comparison among group SAH factors. </sec> <title>OBJECTIVE</title> This paper...