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Open Access

Correction

Correction: Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

  • Eugene Jeong,
  • Namgi Park,
  • Young Choi,
  • Rae Woong Park,
  • Dukyong Yoon
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There is an error in the Funding statement. The correct Funding statement is as follows: This research was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (grant numbers: HI16C0982 and HI17C0970, Government-wide R&D Fund project for infectious disease research, HG18C0067). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Reference

  1. 1. Jeong E, Park N, Choi Y, Park RW, Yoon D (2018) Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals. PLoS ONE 13(11): e0207749. https://doi.org/10.1371/journal.pone.0207749 pmid:30462745

Citation: Jeong E, Park N, Choi Y, Park RW, Yoon D (2019) Correction: Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals. PLoS ONE 14(4): e0215344. https://doi.org/10.1371/journal.pone.0215344

Published: April 9, 2019

Copyright: © 2019 Jeong 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.