Coarse race data conceals disparities in clinical risk score performance

• URL: http://arxiv.org/abs/2304.09270v2
• Date: Thu, 24 Aug 2023 20:01:16 GMT
• Title: Coarse race data conceals disparities in clinical risk score performance
• Authors: Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John Guttag, Nikhil Garg, Emma Pierson
• Abstract summary: We assess clinical risk score performance disparities across 26 granular groups for three outcomes, five risk scores, and four performance metrics. Variation in performance within coarse groups often *exceeds* the variation between coarse groups. Our results suggest that healthcare providers, hospital systems, and machine learning researchers should strive to collect, release, and use granular race data in place of coarse race data.
• Score: 2.53014124471163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown, however, whether this coarse coding conceals meaningful disparities in the performance of clinical risk scores across granular race groups. Here we show that it does. Using data from 418K emergency department visits, we assess clinical risk score performance disparities across 26 granular groups for three outcomes, five risk scores, and four performance metrics. Across outcomes and metrics, we show that the risk scores exhibit significant granular performance disparities within coarse race groups. In fact, variation in performance within coarse groups often *exceeds* the variation between coarse groups. We explore why these disparities arise, finding that outcome rates, feature distributions, and the relationships between features and outcomes all vary significantly across granular groups. Our results suggest that healthcare providers, hospital systems, and machine learning researchers should strive to collect, release, and use granular race data in place of coarse race data, and that existing analyses may significantly underestimate racial disparities in performance.

Related papers

• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery [4.870691561986669]
  Congenital heart defects (CHD) are the most prevalent birth defects in the United States. The outcomes of treatment for CHD differ for specific patient subgroups, with non-Hispanic Black and Hispanic populations experiencing higher rates of mortality and morbidity. We propose a causal inference framework for outcome assessment and leverage advances in transfer learning to incorporate data from both target and source populations.
  arXiv  Detail & Related papers  (2024-03-21T17:20:23Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations [16.782625445546273]
  We demonstrate that in 43% of settings, a model trained on data from two hospitals has poorer worst group accuracy over both hospitals than a model trained on just a single hospital's data. We explain that this phenomenon arises from the spurious correlation that emerges between the disease and hospital, due to hospital-specific image artifacts.
  arXiv  Detail & Related papers  (2023-08-08T17:58:45Z)
• Towards Fair Face Verification: An In-depth Analysis of Demographic Biases [11.191375513738361]
  Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years. However, such systems have been found to exhibit significant biases related to race, age, and gender. This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors.
  arXiv  Detail & Related papers  (2023-07-19T14:49:14Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation [1.6386696247541932]
  "Fairness" in AI refers to assessing algorithms for potential bias based on demographic characteristics such as race and gender. Deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, but no work has yet investigated the fairness of such models. We find statistically significant differences in Dice performance between different racial groups.
  arXiv  Detail & Related papers  (2021-06-23T13:27:35Z)
• Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data [3.7363119896212478]
  We develop a method for modeling large heterogeneous data types relevant to TBI. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. It is used to stratify patients into distinct groups in an unsupervised learning setting.
  arXiv  Detail & Related papers  (2020-12-22T19:31:03Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.