Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Isgut, Monica; Gloster, Logan; Choi, Kyu-Myung; Venugopalan, Janani; Wang, May D.

doi:10.1109/rbme.2022.3216531

“…Errors in ground truth outcomes represent a significant challenge for the development of artificial intelligence (AI) for healthcare applications. The presence of errors in medical datasets can severely hamper the reliability and performance of these AI algorithms 1,2 , potentially placing patients at risk 3 . Good quality, error-free data are also critical for emerging applications such as precision medicine 4 , as well as clinical trial data used to support approvals for new patient treatments 5 .…”

Section: Introductionmentioning

confidence: 99%

Efficient automated error detection in medical data using deep-learning and label-clustering

Nguyen¹,

Diakiw²,

VerMilyea³

et al. 2023

Preprint

0

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Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for detecting errors in data typically focus only on minimizing the effects of random classification noise. More recent progress has focused on using deep-learning to capture errors stemming from subjective labelling and confounding variables, however, such methods can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. Results demonstrated high performance and efficiency on both image- and record-based datasets. Errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete. The resulting trained AI models exhibited greater stability and up to a 45% improvement in accuracy, from 69% to over 99%. These results indicate that practical, automated detection of errors in medical data is possible without human oversight.

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Efficient automated error detection in medical data using deep-learning and label-clustering

Nguyen,

Diakiw,

VerMilyea

et al. 2023

Sci Rep

0

1

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Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories—attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either relabeling them or removing them completely. More recent progress in error detection has focused on using multi-network learning to minimize deleterious effects of errors on training, however, using many neural networks to reach a consensus on which data should be removed can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. For dataset with synthetic label flips added, these errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete compared to a previous model consensus approach developed previously. The resulting trained AI models exhibited greater training stability and up to a 45% improvement in accuracy, from 69 to over 99% compared to the consensus approach, at least 10% improvement on using noise-robust loss functions in a binary classification problem, and a 51% improvement for multi-class classification. These results indicate that practical, automated a priori detection of errors in medical data is possible, without human oversight.

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Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction

Zamitalo

¹

,

Xie

²

,

Allam

³

et al. 2022

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Cited by 10 publications

References 152 publications

Efficient automated error detection in medical data using deep-learning and label-clustering

Efficient automated error detection in medical data using deep-learning and label-clustering

Efficient automated error detection in medical data using deep-learning and label-clustering

Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction

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