Chris Hornung

Chris Hornung
University of Minnesota Twin Cities | UMN · Medical School

Bachelor of Science

About

6
Publications
1,012
Reads
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7
Citations
Citations since 2016
6 Research Items
7 Citations
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Introduction
Medical Student, Class of 2024 at the University of Minnesota Twin Cities. Interests include machine learning in medicine and surgery. Currently investigating sacral nerve modulation therapy and urodynamics for urological disorders with the Functional Urology Discovery Group.

Publications

Publications (6)
Article
Full-text available
Background As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approac...
Article
Full-text available
Purpose: We quantified patient record documentation of sacral neuromodulation (SNM) threshold testing and programming parameters at our institution to identify opportunities to improve therapy outcomes and future SNM technologies. Methods: A retrospective review was conducted using 127 records from 40 SNM patients. Records were screened for SNM...
Article
Full-text available
Purpose: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefor...
Poster
Full-text available
Introduction We assessed YouTube video reliability, quality, and understandability between three rhinosinusitis-related search terms: Sinus Infection (SI), sinusitis (SS), and rhinosinusitis (RS). Methods Reliability, quality, and understandability were assessed using the JAMA score, Surgical Pathology Education Quality Score (SPEQS), and PEMAT-A/V...

Questions

Question (1)
Question
I have a relatively small dataset (~160) with a mix of data types including continuous, nominal, and binary. I am trying to compute a variable (continuous) from that dataset (multiple data types) that maximizes the likelihood of an outcome X (binary). Ideally, the algorithm would be able to scale to a dataset in the range of the ~1000's.

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Projects

Projects (2)
Project
Develop methods for quantifying urodynamics data.
Project
Investigate the influence of SNM stimulation parameters on therapy efficacy. Develop new inputs for device design.