药物坚持中的情感分析:使用基于规则和人工智能驱动的算法来理解患者的药物经验
Wallace Entringer Bottacin1, Alexandre Luquetta2, Luiz Gomes-Jr2
1Postgraduate Program in Pharmaceutical Services and Policies, Federal University of Paraná, Avenida Prefeito Lothário Meissner, 632 - Jardim Botânico, Curitiba, CEP 80210-170, PR, Brazil. wallace.bottacin@gmail.com.
International journal of clinical pharmacy
|October 4, 2024
概括
像VADER和DistilRoBERTa这样的情绪分析模型可以有效地衡量患者对药物的情绪和情绪,改善药房实践. 这些人工智能工具为个性化患者护理策略提供了宝贵的见解.
科学领域:
- 人工智能在药房中的应用
- 自然语言处理应用程序
- 医疗保健中的计算语言学
背景情况:
- 情感分析 (SA) 是药学中未得到充分利用的AI创新,用于评估患者对药物的感受.
- SA利用人工智能和自然语言处理来分析文字的情绪和情绪.
研究的目的:
- 为了评估Valence Aware词典的情感推理 (VADER) 和情感英语蒸罗伯塔基 (DistilRoBERTa) 模型.
- 识别患者对使用SA的药物治疗的情绪和情绪.
主要方法:
- 利用了320,095个匿名患者药物使用经验的数据集.
- 瓦德分析了情绪极性 (-1到+1);迪斯蒂尔罗伯塔将情绪分为七个类别.
- 使用scikit-learn的Python模块进行性能指标.
主要成果:
- VADER实现了0.70的整体准确性,对负面情绪有很强的回忆 (0.80) 和对积极情绪有很好的准确性 (0.73).
- DistilRoBERTa揭示了与药物的有效性,易用性和满意度相关的积极情绪.
- 两种模型都在分析患者药物治疗情绪方面表现出一致和可靠的结果.
结论:
- VADER和DistilRoBERTa成功地分析了患者对药物治疗的情绪和情绪.
- 这些发现支持将SA纳入临床药房,以改善患者护理.
- 这项研究为更个性化,更有效的患者管理策略铺平了道路.
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