結核研究における機械学習:診断,予測,薬剤発見のトレンドのグローバル・ビビロメトリック分析
Siddig Ibrahim Abdelwahab1, Manal Mohamed Elhassan Taha2, Hazem Mathkour3
1Health Research Centre, Jazan University, Jazan, Saudi Arabia. sadiqa@jazanu.edu.sa.
Therapeutic innovation & regulatory science
|August 21, 2025
まとめ
機械学習と結核 (MLTB) 研究は急速に進み,年間22.12%の成長を示しています. この分野は人工知能の技術革新を通じて 新種の結核診断と治療の開発に不可欠です
科学分野:
- 図書館情報
- 人工知能
- 結核の研究
背景:
- 結核 (TB) は世界的に深刻な健康問題であり,新たな診断と治療戦略が必要である.
- 人工知能 (AI),特に機械学習 (ML) は結核研究に革命を起こし,耐薬性予測,放射線学,薬剤発見に影響を与えている.
- この研究は,グローバルな機械学習と結核 (MLTB) 研究の文献分析を提供します.
研究 の 目的:
- 機械学習と結核 (MLTB) に関する世界的な研究の包括的な文献分析を行う.
- 治療革新と規制科学に関連するMLTB研究における主要な動向と協力ネットワークを特定する.
主な方法:
- 2024年5月1日まで,英語のデータベースのMLTBのデータベースを体系的に検索する.
- BiblioshinyとVOSviewerソフトウェアを使用して,図書館計測指標を分析した.
- 出版の動向,引用の影響,国際的協力,主題的クラスタリングに焦点を当てました.
主要な成果:
- MLTB分野は,2000年から2024年までの年間平均成長率22.12%で急速に成長しています.
- 出版物は平均で21.64件の引用を受け,その40.11%は国際協力によるものです.
- ディープラーニング,薬剤開発,バイオインフォマティクス,潜伏結核感染など12の主要な研究分野が特定されました.
結論:
- 機械学習と結核の研究は 分野間の協力とAIの進歩によって 急速に発展しています
- 発見は,将来のAI駆動型結核治療戦略の洞察を提供します.
- 研究の取り組みは,TB対策の規制と翻訳の優先事項とますます一致しています.
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