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Open Access

Correction

Correction: Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice

  • Kevin Lopez,
  • Huan Li,
  • Hyung Paek,
  • Brian Williams,
  • Bidisha Nath,
  • Edward R. Melnick,
  • Andrew J Loza

There are errors in Table 2. The Value of Accuracy should have been 0.79 and the Metric FI should be Weighted F1. Please see the correct Table 2 here.

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Table 2. Model performance at optimal threshold using Youden’s J index.

A 2x2 confusion matrix for the optimal threshold showing physician-month counts and B classification performance statistics. PPV is Positive Predictive Value; NPV is Negative Predictive Value. Weighted F1 was computed as defined in the scikit-learn package (1.0.1).

https://doi.org/10.1371/journal.pone.0315090.t001

Reference

  1. 1. Lopez K, Li H, Paek H, Williams B, Nath B, Melnick ER, et al. (2023) Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice. PLOS ONE 18(2): e0280251. pmid:36724149

Citation: Lopez K, Li H, Paek H, Williams B, Nath B, Melnick ER, et al. (2024) Correction: Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice. PLoS ONE 19(12): e0315090. https://doi.org/10.1371/journal.pone.0315090

Published: December 3, 2024

Copyright: © 2024 Lopez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.