Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

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Bibliographic Details
Title: Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.
Authors: Sri-Iesaranusorn P; Division of Information Science, Nara Institute of Science and Technology, Nara, Japan., Sadahiro R; Department of Immune Medicine, National Cancer Center Research Institute, Tokyo, Japan.; Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan., Murakami S; Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan., Wada S; Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan.; Department of Neuropsychiatry, Nippon Medical School, Tama Nagayama Hospital, Tokyo, Japan., Shimizu K; Department of Psycho-Oncology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan., Yoshida T; Department of Clinical Genomics, National Cancer Center Research Institute, Tokyo, Japan., Aoki K; Department of Immune Medicine, National Cancer Center Research Institute, Tokyo, Japan., Uezono Y; Department of Pain Control Research, The Jikei University School of Medicine, Tokyo, Japan., Matsuoka H; Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan., Ikeda K; Division of Information Science, Nara Institute of Science and Technology, Nara, Japan., Yoshimoto J; Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.; Department of Biomedical Data Science, Fujita Health University School of Medicine, Aichi, Japan.
Source: Frontiers in psychiatry [Front Psychiatry] 2023 Jun 27; Vol. 14, pp. 1205605. Date of Electronic Publication: 2023 Jun 27 (Print Publication: 2023).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101545006 Publication Model: eCollection Cited Medium: Print ISSN: 1664-0640 (Print) Linking ISSN: 16640640 NLM ISO Abbreviation: Front Psychiatry Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1664-0640
DOI:10.3389/fpsyt.2023.1205605