开发一个多变量深度学习模型来预测患者的精神疾病
Archana Mishra1, Biswa Ranjan Mishra2, Debadatta Mohapatra2
1Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, India.
Epilepsy & behavior : E&B
|July 23, 2025
概括
神经网络可以准确地预测患者的精神疾病. 这些模型可以识别有风险的个体,从而实现个性化护理和改善管理结果.
科学领域:
- 神经学 神经学
- 精神病学是一个精神病学.
- 人工智能的人工智能
背景情况:
- 精神疾病在患者中非常普遍,恶化生活质量和治疗结果.
- 早期识别中精神伴随性疾病对于及时干预至关重要.
研究的目的:
- 开发和验证神经网络模型,用于预测患者的精神疾病.
- 利用临床和人口统计数据进行风险评估.
主要方法:
- 对2,258名患者数据 (2013-2023) 的回顾性分析.
- 在R.中使用Keras和神经网络开发神经网络模型.
- 使用准确度,灵敏度,特异性和ROC-AUC评估模型性能;通过SHAP值评估特征重要性.
主要成果:
- 27.6%的患者表现出精神疾病.
- 神经网络模型实现了97.16%的准确性 (ROC-AUC:0.974),超过了卡萨斯模型 (92.4%的准确性,ROC-AUC:0.973).
- 关键预测因素包括发作的年龄,的持续时间和抗药物概况.
结论:
- 神经网络模型准确地预测了中的精神病并发症.
- 这些预测工具可以促进风险人群的早期识别.
- 这些模型支持个性化护理策略,以加强管理.
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