Summary of Development and Validation Of a Machine Learning Algorithm For Clinical Wellness Visit Classification in Cats and Dogs, by Donald Szlosek et al.
Development and Validation of a Machine Learning Algorithm for Clinical Wellness Visit Classification in Cats and Dogs
by Donald Szlosek, Michael Coyne, Julia Riggot, Kevin Knight, DJ McCrann, Dave Kincaid
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an algorithm designed to classify veterinary visits as either “wellness” or “other,” aiming to improve early disease detection in asymptomatic animals. A Gradient Boosting Machine model was trained on a dataset of 11,105 clinical visits from 2012 to 2017 and validated using a random sample of 400 visits. The algorithm demonstrated strong specificity (0.94) and sensitivity (0.86), with a balanced accuracy of 0.90. This study has implications for advancing research on preventive care’s role in subclinical disease identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a tool to help doctors distinguish between routine check-ups and other types of visits at animal hospitals. The goal is to catch potential health problems early, before they become serious. The researchers trained an algorithm using data from over 11,000 visits and tested it on a smaller sample of 400 visits. They found that the algorithm was very good at identifying normal visits (specificity) and also good at correctly identifying visits where there might be a problem (sensitivity). This could help improve animal health by detecting problems early. |
Keywords
» Artificial intelligence » Boosting
