Response to Folweiler KA et al., Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes With Distinct Acute Injury Profiles and Long-Term Outcomes (DOI: 10.1089/neu.2019.6705)

Ching-Ying Wang^{1

2}, Jau-Ching Wu^{1

2}, Yi-Hsuan Kuo^{1

2

3}

Affiliations

¹ Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
² School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
³ Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Comment

Ching-Ying Wang et al. J Neurotrauma. 2024 Jan.

. 2024 Jan;41(1-2):292-293.

doi: 10.1089/neu.2023.0396. Epub 2023 Oct 18.

Ching-Ying Wang^{1

2}, Jau-Ching Wu^{1

2}, Yi-Hsuan Kuo^{1

2

3}

¹ Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
² School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
³ Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

No abstract available

Comment on

Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.
Folweiler KA, Sandsmark DK, Diaz-Arrastia R, Cohen AS, Masino AJ. Folweiler KA, et al. J Neurotrauma. 2020 Jun 15;37(12):1431-1444. doi: 10.1089/neu.2019.6705. Epub 2020 Mar 11. J Neurotrauma. 2020. PMID: 32008422 Free PMC article.