慢性疾患を持つヨルダンの成人の薬物使用量:回帰と機械学習による統合分析
Walid Al-Qerem1, Anan Jarab2, Judith Eberhardt3
1Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan.
Annals of medicine
|August 20, 2025
まとめ
慢性疾患の管理において 患者の健康に関する知識と保険の種類が 薬の服用に大きく影響します 介入は,個人の理解とシステム上の障壁に対処し,従順性を向上させるべきです.
科学分野:
- 医療サービス研究
- デジタル・ヘルス
- 慢性疾患 の 管理
背景:
- 慢性疾患の有効な管理は 患者の薬物使用に大きく依存しています
- 患者さんの健康状態を改善するために 服従に影響を与える要因を理解することが重要です
研究 の 目的:
- 慢性疾患を患っているヨルダン人の成人の薬の服用に 影響する要因を調査する.
- 従来の回帰と機械学習を比較して 適合性を予測する.
主な方法:
- 慢性疾患の患者981人を対象にした 横断的なオンライン調査
- ヘルス・リテラシー・アンケート (HLQ-12) と医薬品アデバーンス・レーティング・スケール (MARS-5) を含む,人口統計的,臨床的,行動的変数を評価した.
- 応用量子回帰と機械学習 (ランダムフォレスト,SHAP分析)
主要な成果:
- 健康に関する知識の向上,糖尿病/心血管疾患の診断,少数の薬の服用は 順守と正に相関していました
- 結婚と公的/軍事的/無保険は従事率の低下と関連していました.
- ランダムフォレストモデルは最も高い精度 (R2 = 0.38) を示した.SHAPでは,健康に関する識字度,疾患の持続時間,および年齢を主要な予測指標として特定した.
結論:
- 医療の知識と保険や治療の複雑さなどの構造的要因に 焦点を当てるべきです
- 健康に関する知識の向上と 治療法の簡素化により 遵守が強化されます
- 説明可能なAIは 資源の限られた環境における 遵守行動に関する新しい洞察を 提供します
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