A cross attention approach to diagnostic explainability using clinical practice guidelines for depression
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Abstract
The lack of explainability in using relevant clinical knowledge hinders the adoption of artificial intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant, untapped Mental Health (MH) data is available in online communities, providing the opportunity to address the explainability problem with substantial potential impact as a screening tool for both online and offline applications. Inspired by how clinicians rely on their expertise when interacting with patients, we leverage relevant clinical knowledge to classify and explain depression-related data, reducing manual review time and engendering trust. We developed a method to enhance attention in contemporary transformer models and generate explanations for classifications that are understandable by mental health practitioners (MHPs) by incorporating external clinical knowledge. We propose a domain-general architecture called ProcesS knowledgeinfused cross ATtention (PSAT) that incorporates clinical practice guidelines (CPG) when computing attention. We transform a CPG resource focused on depression, such as the Patient Health Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable ontology using SNOMED-CT. With this resource, PSAT enhances the ability of models like GPT-3.5 to generate application-relevant explanations. Evaluation of four expert-curated datasets related to depression demonstrates PSAT's applicationrelevant explanations. PSAT surpasses the performance of twelve baseline models and can provide explanations where other baselines fall short.
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Semantic similarity metrics such as BERTScore and domain-specific variants using ClinicalBERT or SciBERT embeddings assess meaning preservation beyond surface forms.
“Semantic similarity metrics such as BERTScore [132] and domainspecific variants using ClinicalBERT [133] or SciBERT embeddings assess meaning preservation beyond surface forms [65,67,70,90,111].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [70]
A first group anchors generated explanations directly to questionnaire items or diagnostic criteria, so that each rationale element traces back to a recognized clinical entry.
“A first group anchors generated explanations directly to questionnaire items or diagnostic criteria, so that each rationale element traces back to a recognized clinical entry such as the Beck Depression Inventory and the DSM-5 [68,71], the PHQ-9 [70], and standardized psychological scales as a class [115].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [70]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733 · see citing works
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works
Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A., Currier, G. W., Melvin, G. A., Greenhill, L., Shen, S., et al. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.10111704 · Cited by 5,157 · cited 2× · see citing works
“…the content from the model’s architecture, we can easily adapt this method to other domains, such as Anxiety, using resources like the Generalized Anxiety Disorder Questionnaire (GAD-7) [7] and Suicide Ideation-Behavior-Attempt using Columbia Suicide Risk Severity Rating Scale (CSSRS) [8].”
MethodKey challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works
ERNIE: Enhanced Language Representation with Informative Entities
Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1139 · Cited by 1,482 · see citing works
Network Dissection: Quantifying Interpretability of Deep Visual Representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.354 · Cited by 1,389 · see citing works
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · UCL Discovery (University College London) · Cited by 1,187 · see citing works
ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding
Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6428 · Cited by 766 · see citing works
Substance Abuse and Mental Health Services Administration
Fisher, G. L., & Roget, N. A. (2009) · Sage eBooks · doi:10.4135/9781412964500.n301 · Cited by 718 · see citing works
Knowledge Enhanced Contextual Word Representations
Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1005 · Cited by 661 · see citing works
Towards Explainable Artificial Intelligence
Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_1 · Cited by 650 · see citing works
Natural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476 · see citing works
Williams, N. (2014) · Occupational Medicine · doi:10.1093/occmed/kqt161 · Cited by 331 · see citing works
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278 · see citing works
Joyce, D. W., Kormilitzin, A., Smith, K., & Cipriani, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00751-9 · Cited by 259 · see citing works
The Development and Psychometric Properties of LIWC-22
Boyd, R. L., Ashokkumar, A., Seraj, S., & Pennebaker, J. W. (2022) · doi:10.13140/rg.2.2.23890.43205 · Cited by 215 · see citing works
Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Shing, H.-C., Nair, S., Zirikly, A., Friedenberg, M., Daumé, H., & Resnik, P. (2018) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w18-0603 · Cited by 214 · see citing works
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
Yang, K., Zhang, T., Kuang, Z., Xie, Q., Huang, J., & Ananiadou, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3648137 · Cited by 214 · see citing works
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., Sheth, A., Welton, R., & Pathak, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313698 · Cited by 193 · see citing works
Self-citationSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., & Sheth, A. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3123028 · Cited by 169 · see citing works
Self-citationGaur, M., Faldu, K., & Sheth, A. (2021) · IEEE Internet Computing · doi:10.1109/mic.2020.3031769 · Cited by 140 · see citing works
Self-citationEarly Identification of Depression Severity Levels on Reddit Using Ordinal Classification
Naseem, U., Dunn, A. G., Kim, J., & Khushi, M. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512128 · Cited by 104 · see citing works
Danesh, S., Sumner, T., & Martin, J. (2015) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s15-1013 · Cited by 102 · see citing works
Learning Rich Representation of Keyphrases from Text
Kulkarni, M., Mahata, D., Arora, R., & Bhowmik, R. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.67 · Cited by 71 · see citing works
ClinicalT5: A Generative Language Model for Clinical Text
Lu, Q., Dou, D., & Nguyen, T. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.398 · Cited by 64 · see citing works
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Nguyen, T. D.-M., Yates, A., Zirikly, A., Desmet, B., & Cohan, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.578 · Cited by 57 · see citing works
Gaur, M., Gunaratna, K., Srinivasan, V., & Jin, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21312 · Cited by 57 · see citing works
Self-citationGlobal Counterfactual Explainer for Graph Neural Networks
Huang, Z., Kosan, M., Medya, S., Ranu, S., & Singh, A. K. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570376 · Cited by 56 · see citing works
Explainable AI in Industry: Practical Challenges and Lessons Learned
Gade, K., Geyik, S., Kenthapadi, K., Mithal, V., & Taly, A. (2020) · Companion Proceedings of the Web Conference 2020 · doi:10.1145/3366424.3383110 · Cited by 46 · see citing works
KALA: Knowledge-Augmented Language Model Adaptation
Kang, M., Baek, J., & Hwang, S. J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.379 · Cited by 39 · see citing works
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
Garg, M., Saxena, C., Saha, S., Krishnan, V., Joshi, R., & Mago, V. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3tbejaye7i8s · Cited by 39 · see citing works
Sarkar, S., Gaur, M., Chen, L. K., Garg, M., & Srivastava, B. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229805 · Cited by 38 · see citing works
Self-citationOn Guaranteed Optimal Robust Explanations for NLP Models
Malfa, E. L., Michelmore, R., Zbrzezny, A. M., Paoletti, N., & Kwiatkowska, M. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/366 · Cited by 37 · see citing works
Gu, X., Wang, Z., Bi, Z., Meng, Y., Liu, L., Han, J., & Shang, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467397 · Cited by 32 · see citing works
Gupta, S., Agarwal, A., Gaur, M., Roy, K., Narayanan, V., Kumaraguru, P., & Sheth, A. (2022) · Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology · doi:10.18653/v1/2022.clpsych-1.12 · Cited by 27 · see citing works
Self-citationKI-BERT: Infusing Knowledge Context for Better Language and Domain UnderstandingPreprint
Faldu, K., Sheth, A., Kikani, P., & Akbari, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08145 · Cited by 22 · see citing works
Self-citationScodari, B. T., Chacko, S. M., Matsumura, R., & Jacobson, N. C. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.004 · Cited by 20 · see citing works
Towards augmenting crisis counselor training by improving message retrieval
DeMasi, O., Hearst, M. A., & Recht, B. (2019) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w19-3001 · Cited by 18 · see citing works
Roy, K., Gaur, M., Soltani, M., Rawte, V., Kalyan, A., & Sheth, A. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2022.1056728 · Cited by 18 · see citing works
Self-citationExplaining Models of Mental Health via Clinically Grounded Auxiliary Tasks
Zirikly, A., & Dredze, M. H. (2022) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/2022.clpsych-1.3 · Cited by 16 · see citing works
Mahbub, M., Begoli, E., Martins, S. B., Peluso, A., Tamang, S. R., & Peterson, G. D. (2023) · IEEE Access · doi:10.1109/access.2023.3235265 · Cited by 9 · see citing works
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
Gunaratna, K., Srinivasan, V., Yerukola, A., & Jin, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.245 · Cited by 9 · see citing works
Artificial and Human Intelligence in Mental Health
Sigman, M., Slezak, D. F., Drucaroff, L. J., Ribeiro, S. T. G., & Carrillo, F. (2021) · AI Magazine · doi:10.1002/j.2371-9621.2021.tb00009.x · Cited by 8 · see citing works
Process Knowledge-Infused Learning for Clinician-Friendly Explanations
Roy, K., Zi, Y., Gaur, M., Malekar, J., Zhang, Q., Narayanan, V., & Sheth, A. (2023) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v1i1.27494 · Cited by 8 · see citing works
Self-citationProcess Knowledge-infused Learning for Suicidality Assessment on Social MediaPreprint
Roy, K., Gaur, M., Zhang, Q., & Sheth, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.12560 · Cited by 7 · see citing works
Self-citationImproving Mental Health Classifier Generalization with Pre-diagnosis Data
Liu, Y., Biester, L., & Mihalcea, R. (2023) · International Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22169 · Cited by 6 · see citing works
Towards Improving the Explainability of Text-based Information Retrieval with Knowledge GraphsPreprint
Chen, B., Chen, K., Yang, Y., Amini, A., Saxena, B., Chávez-García, C., Babaei, M., Feizpour, A., & Varró, D. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.06974 · Cited by 6 · see citing works
A Biomedical Knowledge Graph for Biomarker Discovery in CancerPreprint
Karim, M. R., Comet, L. M., Beyan, O., Rebholz‐Schuhmann, D., & Decker, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.04737 · Cited by 2 · see citing works
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1 of the 53 references couldn’t be matched to a known paper.
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Bojić, L., Zagovora, O., Zelenkauskaitė, A., Vuković, V., Čabarkapa, M., Jerković, S. V., & Jovančević, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-96508-3 · Cited by 100 · see citing works
Trade-offs between machine learning and deep learning for mental illness detection on social media
Ding, Z., Wang, Z., Zhang, Y., Cao, Y., Liu, Y., Shen, X., Tian, Y., & Dai, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-99167-6 · Cited by 68 · see citing works
Kallstenius, T., Capusan, A. J., Andersson, G., & Williamson, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-08031-0 · Cited by 27 · see citing works
Review of Advancements in Depression Detection Using Social Media Data
Dalal, S., Jain, S., & Dave, M. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3448624 · Cited by 24 · see citing works
Self-citationEnhanced large language models for effective screening of depression and anxiety
Liu, J. M., Gao, M., Sabour, S., Chen, Z., Huang, M., & Lee, T. M. C. (2025) · Communications Medicine · doi:10.1038/s43856-025-01158-1 · Cited by 17 · see citing works
Voultsiou, E., & Moussiades, L. (2026) · Electronics · doi:10.3390/electronics15030524 · Cited by 16 · see citing works
Large Language Models and Their Applications in Mental Health: Scoping Review
Lokadjaja, M. C., Kho, J. J., Schulz, P. J., & Goh, W. W. B. (2026) · JMIR Mental Health · doi:10.2196/88057 · Cited by 9 · see citing works
Chen, Y., Gao, Y., Yu, S., Zhao, C., & Lu, Y. (2025) · Journal of Industrial Integration and Management · doi:10.1142/s2424862225300042 · Cited by 5 · see citing works
DepressionFeature: Underlying ontology for user-specific depression analysis
Dalal, S., Jain, S., & Dave, M. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06585-w · Cited by 5 · see citing works
Self-citationGuided and Federated RAG: Architectural Models for Trustworthy AI in Data Spaces
Braga, C. M., Serrano, M., & Fernández‐Medina, E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-10489-2_31 · Cited by 3 · see citing works
Evolving AI: What Lies Beyond Today’s Language Models
Jovanovic, M., & Campbell, M. (2025) · Computer · doi:10.1109/mc.2025.3546045 · Cited by 2 · see citing works
Shah, S. S., Dodiya, T., & Nisha, P. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133395 · Cited by 1 · see citing works
Explaining alerts from a pediatric risk prediction model using clinical text
Nycklemoe, S., Devarapu, S., Gao, Y., Carey, K. A., Kuehnel, N., Munjal, N. K., Jani, P., Churpek, M. M., Dligach, D., Afshar, M., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf121 · Cited by 1 · see citing works
Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
TRUST-MH: Transparent and Responsible User-Level Semantic Tagging for Mental Health Assessment
Dalal, S., & Jain, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13342-7_5
Self-citationProDoKE: An LLM-Guided Prompt Chaining with Domain Knowledge for Depression Detection
Chen, J., Chen, L., Zhao, S., & Xu, C. (2025) · International Conference on Cyberworlds (CW) · doi:10.1109/cw68232.2025.00064
Roy, K., Surana, H., Eswaramoorthi, D. L., Zi, Y., Palit, V., Garimella, R., & Sheth, A. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3805697
Self-citationAnywar, M., Maron, E., & Ross, P. (2026) · Research Square · doi:10.21203/rs.3.rs-9612344/v1
Anywar, M., Maron, E., & Ross, P. (2026) · doi:10.2196/preprints.101569
Arora, S., Barapatre, R., & Paranjape, K. K. (2025) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3793449.3793453
Bao, E., Pérez, A., & Parapar, J. (2026) · Science Progress · doi:10.1177/00368504261467483
NeuroAid: An Open-Data Multimodal Screening Framework for Parkinson’s and Depression Risk EstimationPreprint
Jayesh, R. L., Rajath, U., & Patel, P. (2026) · medRxiv · doi:10.64898/2026.08.03.26359539
- Authors
- Dalal, S., Tilwani, D., Gaur, M., Jain, S., Shalin, V. L., & Sheth, A. P.
- Affiliations
- National Institute of Technology Kurukshetra (IN)
- University of Maryland, Baltimore (US)
- Wright State University (US)
- University of South Carolina (US)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2025-02
- Volume
- 29
- Issue
- 2
- Pages
- 1333-1342
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4403510466
- Cited by
- 19
- References
- 53
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 2
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NSF EAGER (2335967)
- UMBC SURFF Award
- University Grant Commission
- India
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- cross-attention, depression, transformer models, attention mechanism, online mental health communities, PHQ-9, SNOMED CT, knowledge graphs, GPT-3.5, mental health screening, explainable AI
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