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. 2024 Dec 3;19(12):e0315090. doi: 10.1371/journal.pone.0315090

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
PMCID: PMC11614266  PMID: 39625911

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.

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).

A B
Predicted Metric Value
Retained Departed Sensitivity 0.64
Ground Truth Retained 1523 397 Specificity 0.79
Departed 17 30 PPV 0.07
NPV 0.99
Weighted F1 0.86
Accuracy 0.79

Reference

  • 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. doi: 10.1371/journal.pone.0280251 [DOI] [PMC free article] [PubMed] [Google Scholar]

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