Related Experiment Video
Updated: Oct 27, 2025
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
Published on: July 12, 2022
Does It Measure Up?
S Ram Kumar1,2,3
1Division of Cardiac Surgery, Department of Surgery, 12223Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
Insights
Machine learning models show promise for predicting adverse outcomes in pediatric heart surgery. Further comparison with existing risk models is needed to advance quality improvement in congenital heart care.
Area of Science:
- Cardiology
- Medical Informatics
- Surgical Outcomes
Background:
- Measuring outcomes in pediatric cardiac care is a critical but controversial quality improvement initiative.
- Existing risk models, like the Society of Thoracic Surgeons Congenital Heart Surgery Risk Model, predict mortality by accounting for comorbidities.
Purpose of the Study:
- To evaluate machine learning (ML) approaches for predicting adverse outcomes in congenital heart surgery.
- To compare the performance of ML models against established risk models using a shared dataset.
Main Methods:
- Utilized the European Congenital Heart Surgeons Association's congenital database.
- Applied machine learning techniques to predict adverse events in pediatric cardiac surgery patients.
- Compared ML model performance against traditional risk stratification models.
Main Results:
- Machine learning models were investigated for their ability to predict adverse outcomes in congenital heart surgery.
- The study highlights the need for direct comparisons between ML and current risk models on the same data.
Conclusions:
- Machine learning offers a potential new avenue for risk modeling in congenital heart surgery.
- Head-to-head comparisons are essential to understand the relative strengths and weaknesses of ML versus traditional risk models.
- Future risk modeling will likely integrate ML with clinical expertise.
Abstract:
Measuring outcomes in pediatric cardiac care has been one of the more widespread, and at the same time controversial and often polarizing, quality improvement initiatives undertaken in the medical field. Risk models, such as the Society of Thoracic Surgeons Congenital Heart Surgery Risk Model, have been developed to account for comorbidities while predicting the expected mortality for a given surgical encounter. In this issue of the journal, Bertsimas and colleagues report on machine learning approaches to predict adverse outcomes in congenital heart surgery using the European Congenital Heart Surgeons Association's congenital database. A head-to-head comparison of machine learning models and the currently available risk models utilizing the same data set are required to better understand the strengths and weaknesses of each of these approaches. Such a focused analysis will shed light on future approaches for risk modeling, which will undoubtedly continue to benefit from the guidance provided by expert clinical intuition.
Related Concept Videos
01:05Estimation of the Physical Quantities
02:46Uncertainty in Measurement: Reading Instruments
01:29Measures of Intelligence
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
01:18Distance Measurements by Taping
01:19Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
01:52Accuracy and Precision