面向精神分裂症高风险预测，构建医生参与设计的可解释NLP系统。
CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations
- 基于临床访谈文本，融合症状映射与大模型摘要，用BERT分类预测精神病风险。
- 在944份访谈数据上准确率超90%，优于传统基线模型。
- 医生偏好新型图文结合的解释格式，适合临床落地场景。
医疗领域应用自然语言处理工具需要终端用户可理解的可解释性，但传统可解释AI方法与临床推理脱节且缺乏医生参与。本文提出CHiRPE（临床高风险预测可解释性框架），将半结构化临床访谈转录文本转化为：(i) 精神病风险预测；(ii) 与临床医生共同设计的新型SHAP解释形式。该系统基于AMP-SCZ研究中24个国际诊所的944份半结构化访谈数据训练，整合症状域映射、大模型摘要与BERT分类。三种BERT变体均实现超过90%的准确率，显著优于基线模型。28位临床专家评估显示，对本研究提出的概念引导型解释，尤其是图文混合摘要形式有强烈偏好。CHiRPE证明，以临床为导向的模型开发可同时实现高精度与强可解释性。下一步将开展跨24个国际站点的真实世界验证。
原文摘要 · Abstract (English)
The medical adoption of NLP tools requires interpretability by end users, yet traditional explainable AI (XAI) methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with Explainability), an NLP pipeline that takes transcribed semi-structured clinical interviews to: (i) predict psychosis risk; and (ii) generate novel SHAP explanation formats co-developed with clinicians. Trained on 944 semi-structured interview transcripts across 24 international clinics of the AMP-SCZ study, the CHiRPE pipeline integrates symptom-domain mapping, LLM summarisation, and BERT classification. CHiRPE achieved over 90% accuracy across three BERT variants and outperformed baseline models. Explanation formats were evaluated by 28 clinical experts who indicated a strong preference for our novel concept-guided explanations, especially hybrid graph-and-text summary formats. CHiRPE demonstrates that clinically-guided model development produces both accurate and interpretable results. Our next step is focused on real-world testing across our 24 international sites.
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