病気の自動化された国際分類 暗号化:体系的なレビュー
Seyyedeh Fatemeh Mousavi Baigi1,2, Masoumeh Sarbaz1, Ali Darroudi1,2
1Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
Journal of medical signals and sensors
|August 27, 2025
まとめ
人工知能 (AI) は,国際疾病分類 (ICD) の自動化された臨床コーディングを強化します. データの不均衡などの課題にもかかわらず AIは医療コードの正確性と効率性などの利点を提供します
科学分野:
- 医療情報学
- 医療における人工知能
- クリニカル・コーディング・システム
背景:
- 人工知能を用いた 自動化された臨床コーディングは 医療の効率化に不可欠です
- 国際疾病分類 (ICD) のコードの正確さは,請求書と研究に不可欠です.
- 既存の手動コーディングプロセスは,スピードと一貫性において課題に直面しています.
研究 の 目的:
- AIベースの自動ICDコーディングに関する現在の知識を体系的にレビューする.
- この分野における課題,利益,将来の研究方向を特定し,統合する.
- 自動化されたICDコーディングシステムを進めるための洞察を提供すること.
主な方法:
- 2024年1月1日に主要なデータベース (PubMed,Embase,Scopus,Web of Science) で実施された体系的な文献検索.
- 体系的なレビューとメタ分析 (PRISMA) のガイドラインの優先報告項目の遵守
- AI主導のICDコーディングの課題,利点,および研究ギャップに焦点を当てた研究の含有; 12,641のレコードから選択された8つの研究の分析.
主要な成果:
- 6つの主要な課題が特定されました: 広大なラベルスペース,不均衡なデータ,長い文書,解釈可能性,倫理的な懸念,透明性の欠如
- 意思決定の改善,データ標準化,コードの精度向上など10の主要な利点が強調されました.
- 8つの将来の研究方向が提案されています:学際的な協力,学習の移転,透明性,積極的な学習.
結論:
- AIベースの自動ICDコーディングは 臨床コーディングに革命をもたらす大きな可能性を秘めています
- 課題を解決することが 実施の成功に不可欠です
- AI駆動のコーディングシステムを最適化するために,転送学習や透明性などの分野でのさらなる研究が推奨されています.
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