Medicine and Health Sciences Commons^™

Missing Lateral Relationships In Top‑Level Concepts Of An Ontology, Ling Zheng, Yan Chen, Hua Min, P. Lloyd Hildebrand, Hao Liu, Michael Halper, James Geller, Sherri De Coronado, Yehoshua Perl

Publications and Research

Background: Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many health information processing environments. Hence, quality assurance of the conceptual content of any ontology is critical. Relationships are foundational to the definition of concepts. Missing relationship errors (i.e., unintended omissions of important definitional relationships) can have a deleterious effect on the quality of an ontology. An abstraction network is a structure that overlays an ontology and provides an alternate, summarization view of its contents. One kind of abstraction network is …

Outlier Concepts Auditing Methodology For A Large Family Of Biomedical Ontologies, Ling Zheng, Hua Min, Yan Chen, Vipina Keloth, James Geller, Yehoshua Perl, George Hripcsak

Publications and Research

Background: Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies).

Methods: To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to …

Development Of A Hospital Readmission Reduction Program For Patients Discharged To Skilled Nursing Facilities: An Application Of Artificial Intelligence And Machine Learning Techniques, Anna Stachel

Dissertations and Theses

Background

Hospital readmissions within 30 days after discharge have drawn national policy attention as they are a reflection of suboptimal patient care. Readmissions are costly, accounting for more than $17 billion in potentially avoidable Medicare expenditures - nearly 78% of readmissions may be avoidable. Rich electronic data from medical records, growing computing capacities, and open source machine learning algorithms offer new opportunities to predict patients at high risk for readmission and prevent readmission through focused interventions. Prediction models might also serve to provide a more nuanced context of patient characteristics that lead to variations in readmission rates. Furthermore, transitional care …

Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar

Student Theses and Dissertations

The healthcare industry is primed for a massive transformation in the coming decades due to emerging technologies such as Artificial Intelligence (AI) and Machine Learning. With a practical application to the UNOS (United Network of Organ Sharing) database, this Thesis seeks to investigate how Machine Learning and analytic methods may be used to predict one-year heart transplantation outcomes. This study also sought to improve on predictive performances from prior studies by analyzing both Donor and Recipient data. Models built with algorithms such as Stacking and Tree Boosting gave the highest performance, with AUC’s of 0.6810 and 0.6804, respectively. In this …

Feminist Pedagogy In A Time Of Coronavirus Pandemic, Alexandra Juhasz, Laura Wexler, Liz Losh, Sharon Irish

Publications and Research

FemTechNet, a network of scholars, artists, and students working on, with, and at the borders of technology, science, and feminism, has a great deal of experience thinking about pedagogy and technology. We have produced real intimacy, vibrant classes, and insurgent pedagogy since 2012. The principles of our signature Distributed Open Collaborative Courses (DOCCs) are crucial (see below). In this time of crisis, we believe we need to think again, drawing the most power possible from the radical knowledges, tactics, and commitments of feminist pedagogies of past experience. We write while schools, colleges, and universities have closed in a cascade of …

Leveraging Technology To Blend Large-Scale Epidemiologic Surveillance With Social And Behavioral Science Methods: Successes, Challenges, And Lessons Learned Implementing The Unite Longitudinal Cohort Study Of Hiv Risk Factors Among Sexual Minority Men In The United States, H. Jonathon Rendina, Ali J. Talan, Nicola F. Tavella, Jonathan Lopez Matos, Ruben H. Jimenez, S. Scott Jones, Brian Salfas, Drew Westmoreland

Publications and Research

The use of digital technologies to conduct large-scale research with limited interaction (i.e., no in-person contact) and objective endpoints (i.e., biological testing) has significant potential for the field of epidemiology, but limited research to date has been published on the successes and challenges of such approaches. We analyzed data from a cohort study of sexual minority men across the United States, collected using digital strategies during a 10-month period from 2017 to 2018. Overall, 113,874 individuals were screened, of whom 26,000 were invited to the study, 10,691 joined the study, and 7,957 completed all enrollment steps, including return of a …