Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models
Trauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy tradeoff, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2023-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133358 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849734568252276736 |
|---|---|
| author | Katherine Phillips Katherine Brown Steve Talbert Doug Talbert |
| author_facet | Katherine Phillips Katherine Brown Steve Talbert Doug Talbert |
| author_sort | Katherine Phillips |
| collection | DOAJ |
| description | Trauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy tradeoff, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I Trauma Center’s registry cases (n=50,644), this study describes, uses, and derives observations from a methodology to more thoroughly examine this tradeoff. This or similar methods can provide the insight needed for practitioners to balance understandability with accuracy. Additionally, this study incorporates an evaluation of group-based fairness into this tradeoff analysis to provide an additional dimension of insight into model selection. The experiments allow us to draw several conclusions regarding the machine learning models in the domain of trauma triage and demonstrate the value of our tradeoff analysis to provide insight into choices regarding model complexity, model accuracy, and model fairness. |
| format | Article |
| id | doaj-art-b2edfbfb829e44a0af61baaf06374f08 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-b2edfbfb829e44a0af61baaf06374f082025-08-20T03:07:45ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13335869664Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage ModelsKatherine PhillipsKatherine BrownSteve TalbertDoug TalbertTrauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy tradeoff, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I Trauma Center’s registry cases (n=50,644), this study describes, uses, and derives observations from a methodology to more thoroughly examine this tradeoff. This or similar methods can provide the insight needed for practitioners to balance understandability with accuracy. Additionally, this study incorporates an evaluation of group-based fairness into this tradeoff analysis to provide an additional dimension of insight into model selection. The experiments allow us to draw several conclusions regarding the machine learning models in the domain of trauma triage and demonstrate the value of our tradeoff analysis to provide insight into choices regarding model complexity, model accuracy, and model fairness.https://journals.flvc.org/FLAIRS/article/view/133358 |
| spellingShingle | Katherine Phillips Katherine Brown Steve Talbert Doug Talbert Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models |
| title_full | Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models |
| title_fullStr | Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models |
| title_full_unstemmed | Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models |
| title_short | Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models |
| title_sort | group bias and the complexity accuracy tradeoff in machine learning based trauma triage models |
| url | https://journals.flvc.org/FLAIRS/article/view/133358 |
| work_keys_str_mv | AT katherinephillips groupbiasandthecomplexityaccuracytradeoffinmachinelearningbasedtraumatriagemodels AT katherinebrown groupbiasandthecomplexityaccuracytradeoffinmachinelearningbasedtraumatriagemodels AT stevetalbert groupbiasandthecomplexityaccuracytradeoffinmachinelearningbasedtraumatriagemodels AT dougtalbert groupbiasandthecomplexityaccuracytradeoffinmachinelearningbasedtraumatriagemodels |