OpenNovelty.org

From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Psychiatric ComorbidityDiagnostic DialogueEMR DatasetMulti-Agent Simulation
Abstract:

Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper contributes a synthetic EMR dataset (PsyCoProfile) and a multi-agent framework for generating diagnostic dialogues specifically addressing psychiatric comorbidity, culminating in the PsyCoTalk dataset of 3,000 validated dialogues. It resides in the 'EMR-Based Synthetic Dialogue Construction' leaf, which contains only two papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Synthetic Data Generation and Clinical Grounding,' distinguishing it from therapeutic chatbot systems and dialogical treatment approaches that dominate other branches of the field.

The taxonomy reveals neighboring work in 'Benchmark Dataset Development' (one paper providing evaluation resources without EMR grounding) and 'DSM-ICD Aligned Diagnostic Models' (one paper on clinical reasoning integration). The paper's focus on comorbidity connects it to 'Comorbidity Clinical Guides' (clinical frameworks rather than automated systems) and contrasts with 'Therapeutic Chatbot Systems' emphasizing empathetic response over diagnostic accuracy. The scope notes clarify that EMR-based dialogue construction excludes general benchmarks and therapeutic intervention frameworks, positioning this work at the intersection of synthetic data generation and clinical diagnostic protocols.

Among ten candidates examined across three contributions, none were identified as clearly refuting the work. The PsyCoProfile EMR dataset had zero candidates examined, suggesting limited directly comparable prior work in synthetic comorbidity EMR construction. The multi-agent framework examined one candidate without refutation, while PsyCoTalk examined nine candidates, all classified as non-refutable or unclear. This limited search scope (ten total candidates, not hundreds) suggests the analysis captures immediate semantic neighbors but cannot claim exhaustive coverage of all potentially relevant psychiatric dialogue generation literature.

Based on the top-ten semantic matches and taxonomy structure, the work appears to occupy a relatively underexplored niche combining EMR-based synthesis with comorbidity-specific diagnostic dialogue generation. The sparse population of its taxonomy leaf and absence of refuting candidates within the examined scope suggest novelty, though the limited search scale means potentially relevant work in adjacent clinical NLP domains may exist beyond these candidates.

Taxonomy

Core-task Taxonomy Papers
11
3
Claimed Contributions
10
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Generating diagnostic dialogues for psychiatric comorbidity. The field encompasses diverse approaches to mental health support and assessment, organized into several main branches. Synthetic Data Generation and Clinical Grounding focuses on creating realistic dialogue datasets anchored in clinical records and benchmarks, exemplified by works like MentalChat Benchmark[1] and Conversational Diagnosis LLM[9]. Clinical Reasoning and Diagnostic Integration addresses the complexities of comorbid conditions and self-diagnosis practices, as seen in Comorbid Mental Illness[10] and Rethinking Self Diagnosis[7]. Therapeutic Chatbot Systems explores AI-driven conversational agents for mental health support, including Psychology Supportive Chatbots[11] and Beyond Empathy[2]. Dialogical and Network-Based Therapeutic Approaches emphasizes collaborative, dialogue-centered methods rooted in clinical practice, represented by Open Dialogue Approach[6] and Dialogic Practice Elements[4]. Finally, Resource-Oriented Therapeutic Models highlights strength-based frameworks like Resource Oriented Therapeutic[3]. These branches reflect a spectrum from data-driven computational methods to human-centered therapeutic philosophies. A particularly active tension exists between synthetic data construction for training diagnostic systems and the therapeutic emphasis on open, non-directive dialogue. Works in synthetic generation aim to produce scalable, clinically grounded training material, while dialogical approaches prioritize relational dynamics and polyphonic conversation. Diagnostic Dialogues[0] sits squarely within the EMR-Based Synthetic Dialogue Construction cluster, closely aligned with Conversational Diagnosis LLM[9], both leveraging electronic medical records to generate realistic diagnostic exchanges. Compared to MentalChat Benchmark[1], which provides broad evaluation datasets, Diagnostic Dialogues[0] emphasizes the specific challenge of comorbidity, requiring nuanced representation of overlapping symptom patterns. This focus distinguishes it from therapeutic chatbot work like Beyond Empathy[2], which prioritizes empathetic response generation over diagnostic accuracy, highlighting an ongoing question of how computational models can balance clinical precision with conversational naturalness.

Claimed Contributions

PsyCoProfile: Synthetic EMR dataset for psychiatric comorbidity

The authors introduce PsyCoProfile, a dataset of 502 synthetic electronic medical records (EMRs) constructed from social media posts. These EMRs cover six comorbidity combinations involving Depression, Anxiety, Bipolar, and ADHD, and include detailed personal experiences to support realistic dialogue generation.

0 retrieved papers
Multi-agent framework with HDSM and DCT for diagnostic dialogue generation

The authors develop a multi-agent system that integrates a Hierarchical Diagnostic State Machine and Diagnostic Context Tree based on SCID-5-RV clinical interview standards. This framework guides doctor, patient, and tool agents through over 130 diagnostic states to generate clinically coherent multi-turn dialogues.

1 retrieved paper
PsyCoTalk: Large-scale diagnostic dialogue dataset for psychiatric comorbidity

The authors present PsyCoTalk, the first large-scale dialogue dataset specifically designed for psychiatric comorbidity research. It contains 3,000 multi-turn diagnostic conversations validated by psychiatrists, offering greater length and clinical depth than existing single-disorder datasets.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PsyCoProfile: Synthetic EMR dataset for psychiatric comorbidity

The authors introduce PsyCoProfile, a dataset of 502 synthetic electronic medical records (EMRs) constructed from social media posts. These EMRs cover six comorbidity combinations involving Depression, Anxiety, Bipolar, and ADHD, and include detailed personal experiences to support realistic dialogue generation.

Contribution

Multi-agent framework with HDSM and DCT for diagnostic dialogue generation

The authors develop a multi-agent system that integrates a Hierarchical Diagnostic State Machine and Diagnostic Context Tree based on SCID-5-RV clinical interview standards. This framework guides doctor, patient, and tool agents through over 130 diagnostic states to generate clinically coherent multi-turn dialogues.

Contribution

PsyCoTalk: Large-scale diagnostic dialogue dataset for psychiatric comorbidity

The authors present PsyCoTalk, the first large-scale dialogue dataset specifically designed for psychiatric comorbidity research. It contains 3,000 multi-turn diagnostic conversations validated by psychiatrists, offering greater length and clinical depth than existing single-disorder datasets.

Table of Contents