Summary of An Interoperable Machine Learning Pipeline For Pediatric Obesity Risk Estimation, by Hamed Fayyaz et al.
An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation
by Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H. Timothy Bunnell, Thao-Ly T. Phan, Rahmatollah Beheshti
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 a novel end-to-end pipeline for predicting pediatric obesity risk using machine learning (ML) models. This pipeline is designed to support data extraction, inference, and communication via an API or user interface. The study focuses on routinely recorded data in pediatric electronic health records (EHRs), leveraging a diverse list of medical concepts to predict the 1-3 year risk of developing obesity. To facilitate integration with different EHR systems, the pipeline utilizes the Fast Healthcare Interoperability Resources (FHIR) standard. The paper reports the effectiveness of the predictive model and its alignment with feedback from stakeholders including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a special tool that helps doctors predict if kids will become obese in the future. It uses machine learning to look at information already stored in electronic health records (EHRs) to make predictions about the risk of obesity. The goal is to help doctors take action early on to prevent obesity, which can be really bad for kids’ health. The tool also makes it easy for different EHR systems to work together. |
Keywords
* Artificial intelligence * Alignment * Inference * Machine learning
