@inproceedings{fong-etal-2026-chirpe,
title = "{CH}i{RPE}: A Step Towards Real-World Clinical {NLP} with Clinician-Oriented Model Explanations",
author = "Fong, Stephanie and
Wang, Zimu and
Oliveira, Guilherme C and
Zhao, Xiangyu and
Jiang, Yiwen and
Liu, Jiahe and
Colton, Beau-Luke and
Woods, Scott W. and
Shenton, Martha and
Nelson, Barnaby and
Ge, Zongyuan and
Dwyer, Dominic",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-short.46/",
pages = "646--658",
ISBN = "979-8-89176-381-4",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations
%A Fong, Stephanie
%A Wang, Zimu
%A Oliveira, Guilherme C.
%A Zhao, Xiangyu
%A Jiang, Yiwen
%A Liu, Jiahe
%A Colton, Beau-Luke
%A Woods, Scott W.
%A Shenton, Martha
%A Nelson, Barnaby
%A Ge, Zongyuan
%A Dwyer, Dominic
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F fong-etal-2026-chirpe
%X 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.
%U https://aclanthology.org/2026.eacl-short.46/
%P 646-658
Markdown (Informal)
[CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations](https://aclanthology.org/2026.eacl-short.46/) (Fong et al., EACL 2026)
ACL
- Stephanie Fong, Zimu Wang, Guilherme C Oliveira, Xiangyu Zhao, Yiwen Jiang, Jiahe Liu, Beau-Luke Colton, Scott W. Woods, Martha Shenton, Barnaby Nelson, Zongyuan Ge, and Dominic Dwyer. 2026. CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 646–658, Rabat, Morocco. Association for Computational Linguistics.