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Volume 19
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Correction to Int. J. Environ. Res. Public Health 2021, 18(5), 2600.
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Correction

Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600

by
Ioannis N. Anastopoulos
1,2,
Chloe K. Herczeg
2,
Kasey N. Davis
2 and
Atray C. Dixit
2,*
1
Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA
2
Coral Genomics, Inc., 953 Indiana St., San Francisco, CA 94107, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(7), 4216; https://doi.org/10.3390/ijerph19074216
Submission received: 17 February 2022 / Accepted: 25 February 2022 / Published: 1 April 2022
Browse Figure
Versions Notes
In the original publication [1], there was a mistake in the colors associated with Figure 4A. The colors of the boxplots were incorrect.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.

Reference

  1. Anastopoulos, I.N.; Herczeg, C.K.; Davis, K.N.; Dixit, A.C. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600. [Google Scholar] [CrossRef] [PubMed]
Ijerph 19 04216 g001 550
Figure 4. Performance comparisons on the FAERS dataset. (A) Predictive utility of various features and model architectures for predicting adverse events in the FAERS dataset. X-axis labels correspond to adverse event categories for a particular case. Y-axis is the AUC at predicting each of the labels. Colors correspond to various feature subsets tested. Error bars correspond to 95% confidence interval derived from bootstrapping on 5-fold cross-validation (each fold contains 28,682 records). (B) Power analysis demonstrating improvement in performance as a function of the number of patient records examined. Blue corresponds to hospitalization model performance and orange corresponds to performance of model predicting death. X-axis is log10 (number of records) Y-axis is AUC. Shaded error region corresponds to 95% confidence interval derived from bootstrapping on 5-fold cross-validation in a subsampled dataset corresponding to the X-axis location. (C) Plot demonstrating relationship between model error across all outcomes and age, (D) average molecular weight of drugs patient is taking, and (E) patient sex.
Figure 4. Performance comparisons on the FAERS dataset. (A) Predictive utility of various features and model architectures for predicting adverse events in the FAERS dataset. X-axis labels correspond to adverse event categories for a particular case. Y-axis is the AUC at predicting each of the labels. Colors correspond to various feature subsets tested. Error bars correspond to 95% confidence interval derived from bootstrapping on 5-fold cross-validation (each fold contains 28,682 records). (B) Power analysis demonstrating improvement in performance as a function of the number of patient records examined. Blue corresponds to hospitalization model performance and orange corresponds to performance of model predicting death. X-axis is log10 (number of records) Y-axis is AUC. Shaded error region corresponds to 95% confidence interval derived from bootstrapping on 5-fold cross-validation in a subsampled dataset corresponding to the X-axis location. (C) Plot demonstrating relationship between model error across all outcomes and age, (D) average molecular weight of drugs patient is taking, and (E) patient sex.
Ijerph 19 04216 g001
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MDPI and ACS Style

Anastopoulos, I.N.; Herczeg, C.K.; Davis, K.N.; Dixit, A.C. Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600. Int. J. Environ. Res. Public Health 2022, 19, 4216. https://doi.org/10.3390/ijerph19074216

AMA Style

Anastopoulos IN, Herczeg CK, Davis KN, Dixit AC. Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600. International Journal of Environmental Research and Public Health. 2022; 19(7):4216. https://doi.org/10.3390/ijerph19074216

Chicago/Turabian Style

Anastopoulos, Ioannis N., Chloe K. Herczeg, Kasey N. Davis, and Atray C. Dixit. 2022. "Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600" International Journal of Environmental Research and Public Health 19, no. 7: 4216. https://doi.org/10.3390/ijerph19074216

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