@inproceedings{li-etal-2026-beyond-binary,
title = "Beyond Binary {ADHD} Classification: Multimodal Phenotyping in a Clinical Psychiatric Cohort",
author = "Li, Siyu and
Perkoff, E. Margaret and
Ungar, Lyle",
editor = "Wang, Zimu and
Jiang, Yiwen and
Shen, Yaling and
Zhao, Xiangyu and
Fong, Stephanie and
Sun, Xin and
Zhou, Yi and
Ji, Shaoxiong and
Desimone, Robert and
Nelson, Barnaby and
Dwyer, Dominic and
Ge, Zongyuan",
booktitle = "Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy ({M}ulti{P}syche)",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.multipsyche-1.6/",
pages = "55--68",
ISBN = "979-8-89176-448-4",
abstract = "High-quality automated ADHD assessment has the potential to increase screening rates among individuals in clinical settings, but these methods are far from perfect. Binary classification methods can obscure differences among clinical populations, and computational ADHD studies often evaluate binary diagnostic prediction against neurotypical controls. Such comparisons can obscure which measurements remain useful when ADHD is distinguished from other psychiatric conditions. We address these gaps using the HYPERAKTIV clinical cohort and four measurements: the Conners Continuous Performance Test II (CPT-II), self-report questionnaires, heart-rate variability (HRV), and actigraphy. We examine three aspects of multimodal ADHD assessments: what information different modalities provide for binary ADHD classification, how well multimodal measurements distinguish the clinical groups, and how patterns in the features relate to clinical diagnosis in unsupervised analysis. CPT-II combined with self-report achieved the highest binary AUC (0.908), but was not reliably better than self-report alone under nested cross-validation. In the three-way task, the inattentive group was the hardest to identify. Clustering produced feature profiles that crossed diagnostic groups. These results suggest that adding more modalities does not necessarily improve classification and that feature-space structure does not always align with diagnostic groups."
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%0 Conference Proceedings
%T Beyond Binary ADHD Classification: Multimodal Phenotyping in a Clinical Psychiatric Cohort
%A Li, Siyu
%A Perkoff, E. Margaret
%A Ungar, Lyle
%Y Wang, Zimu
%Y Jiang, Yiwen
%Y Shen, Yaling
%Y Zhao, Xiangyu
%Y Fong, Stephanie
%Y Sun, Xin
%Y Zhou, Yi
%Y Ji, Shaoxiong
%Y Desimone, Robert
%Y Nelson, Barnaby
%Y Dwyer, Dominic
%Y Ge, Zongyuan
%S Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy (MultiPsyche)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-448-4
%F li-etal-2026-beyond-binary
%X High-quality automated ADHD assessment has the potential to increase screening rates among individuals in clinical settings, but these methods are far from perfect. Binary classification methods can obscure differences among clinical populations, and computational ADHD studies often evaluate binary diagnostic prediction against neurotypical controls. Such comparisons can obscure which measurements remain useful when ADHD is distinguished from other psychiatric conditions. We address these gaps using the HYPERAKTIV clinical cohort and four measurements: the Conners Continuous Performance Test II (CPT-II), self-report questionnaires, heart-rate variability (HRV), and actigraphy. We examine three aspects of multimodal ADHD assessments: what information different modalities provide for binary ADHD classification, how well multimodal measurements distinguish the clinical groups, and how patterns in the features relate to clinical diagnosis in unsupervised analysis. CPT-II combined with self-report achieved the highest binary AUC (0.908), but was not reliably better than self-report alone under nested cross-validation. In the three-way task, the inattentive group was the hardest to identify. Clustering produced feature profiles that crossed diagnostic groups. These results suggest that adding more modalities does not necessarily improve classification and that feature-space structure does not always align with diagnostic groups.
%U https://aclanthology.org/2026.multipsyche-1.6/
%P 55-68
Markdown (Informal)
[Beyond Binary ADHD Classification: Multimodal Phenotyping in a Clinical Psychiatric Cohort](https://aclanthology.org/2026.multipsyche-1.6/) (Li et al., MultiPsyche 2026)
ACL