Publications by authors named "R Alex Hsi"

To develop and validate a high-fidelity, nonbiohazardous simulator model for the ultrasound-guided percutaneous nephrolithotomy procedure. We employed a systematic framework based on Delphi consensus and modern education theory to design a simulation model. Twelve expert surgeons provided input through a hierarchal task analysis and identified procedural tasks, anatomical landmarks, and potential errors.

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Background: Recent advancements of large language models (LLMs) like Generative Pre-trained Transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. This study investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were caused by symptomatic kidney stones.

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Article Synopsis
  • This study investigates the genetic factors contributing to kidney stone disease using a large-scale analysis of electronic health records from over 5,000 cases and 83,000 controls.
  • The research identified ten significant genetic loci related to kidney stones, with the most notable one being rs28544423, which influences urinary excretion and is linked to calcium oxalate stones.
  • While important genetic associations were found, the study noted some limitations including potential biases and concluded that genetic variants influence stone composition but not the severity of the disease.
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Article Synopsis
  • PheMIME is an interactive visualization tool developed to analyze and characterize multimorbidity patterns across different populations using data from large-scale electronic health record (EHR) systems.
  • It integrates data from institutions like Vanderbilt University and Mass General Brigham, allowing users to explore complex disease relationships through dynamic, multi-faceted visualizations and analyses.
  • The tool enhances our understanding of patient health by making it easier to identify disease associations, ultimately contributing to more personalized healthcare strategies.
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