Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models.

Journal: BMC medical informatics and decision making

PMID: 36229835

Abstract

BACKGROUND: Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, searching for this knowledge is very labor-intensive. In the current study, using natural language processing (NLP), we analyzed medical research corpora related to recurrent glioblastoma to find potential targets and treatments.

Authors

Bum-Sup Jang

Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.
Andrew J Park

Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Tuck School of Business, Dartmouth College, Hanover, New Hampshire.
In Ah Kim

Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, 13620, Korea.

Keywords

Bevacizumab Chronic Disease Clinical Trials as Topic Deep Learning Glioblastoma Humans Lomustine Natural Language Processing Nivolumab Temozolomide

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Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models.

Abstract

Authors

Keywords

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