@inproceedings{nguyen-etal-2026-fhexchange,
title = "{FH}exchange: Resources for Family Health History Extraction and Normalization From Consumer Dialog Sources",
author = "Nguyen, Michelle and
Soley, Nidhi and
Zirikly, Ayah and
Sedoc, Jo{\~a}o and
Taylor, Casey",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2026",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-1.82/",
pages = "1014--1028",
ISBN = "979-8-89176-434-7",
abstract = "Family health history (FHx) offers insight into a person{'}s health and disease risk, but it is largely held within free-text clinical formats that require processing for maximal utility of the data. The rapid deployment of ambient AI scribes and conversational agents in clinical settings necessitates evaluation on dynamic patient-clinician and patient-agent dialogs. To address this gap, we introduce two new datasets of patient FHx dialog documents designed to benchmark information extraction and entity linking. Distinct from clinician-entered datasets, patient-reported dialog data has its own semantic and content characteristics, which need to be studied for more patient-centered healthcare. We contribute a publicly available resource called FHexchange, with new annotations for family members, clinical observations, related entities, and standardized UMLS CUIs, offering the clinical NLP community a robust evaluation bed for emerging generative AI tools."
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<abstract>Family health history (FHx) offers insight into a person’s health and disease risk, but it is largely held within free-text clinical formats that require processing for maximal utility of the data. The rapid deployment of ambient AI scribes and conversational agents in clinical settings necessitates evaluation on dynamic patient-clinician and patient-agent dialogs. To address this gap, we introduce two new datasets of patient FHx dialog documents designed to benchmark information extraction and entity linking. Distinct from clinician-entered datasets, patient-reported dialog data has its own semantic and content characteristics, which need to be studied for more patient-centered healthcare. We contribute a publicly available resource called FHexchange, with new annotations for family members, clinical observations, related entities, and standardized UMLS CUIs, offering the clinical NLP community a robust evaluation bed for emerging generative AI tools.</abstract>
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%0 Conference Proceedings
%T FHexchange: Resources for Family Health History Extraction and Normalization From Consumer Dialog Sources
%A Nguyen, Michelle
%A Soley, Nidhi
%A Zirikly, Ayah
%A Sedoc, João
%A Taylor, Casey
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S BioNLP 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-434-7
%F nguyen-etal-2026-fhexchange
%X Family health history (FHx) offers insight into a person’s health and disease risk, but it is largely held within free-text clinical formats that require processing for maximal utility of the data. The rapid deployment of ambient AI scribes and conversational agents in clinical settings necessitates evaluation on dynamic patient-clinician and patient-agent dialogs. To address this gap, we introduce two new datasets of patient FHx dialog documents designed to benchmark information extraction and entity linking. Distinct from clinician-entered datasets, patient-reported dialog data has its own semantic and content characteristics, which need to be studied for more patient-centered healthcare. We contribute a publicly available resource called FHexchange, with new annotations for family members, clinical observations, related entities, and standardized UMLS CUIs, offering the clinical NLP community a robust evaluation bed for emerging generative AI tools.
%U https://aclanthology.org/2026.bionlp-1.82/
%P 1014-1028
Markdown (Informal)
[FHexchange: Resources for Family Health History Extraction and Normalization From Consumer Dialog Sources](https://aclanthology.org/2026.bionlp-1.82/) (Nguyen et al., BioNLP 2026)
ACL