mlsanityData to 14 Oct 2026

ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data

arXiv 2505.14038 ↗First posted 20 May 2025cs.AI cs.CLcited by 6

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Mental health risk is a critical global public health challenge, necessitating innovative and reliable assessment methods. With the development of large language models (LLMs), they stand out to be a promising tool for explainable mental health care applications. Nevertheless, existing approaches predominantly rely on subjective textual mental records, which can be distorted by inherent mental uncertainties, leading to inconsistent and unreliable predictions. To address these limitations, this paper introduces ProMind-LLM. We investigate an innovative approach integrating objective behavior data as complementary information alongside subjective mental records for robust mental health risk assessment. Specifically, ProMind-LLM incorporates a comprehensive pipeline that includes domain-specific pretraining to tailor the LLM for mental health contexts, a self-refine mechanism to optimize the processing of numerical behavioral data, and causal chain-of-thought reasoning to enhance the reliability and interpretability of its predictions. Evaluations of two real-world datasets, PMData and Globem, demonstrate the effectiveness of our proposed methods, achieving substantial improvements over general LLMs. We anticipate that ProMind-LLM will pave the way for more dependable, interpretable, and scalable mental health case solutions.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 37 of its 72 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

37 of 72 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  2. 2026Responsible Evaluation of AI for Mental Healthcited by 2
  3. 2026TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Healthcited by 2
  4. 2026A Multi-Agent Framework for Interpreting Multivariate Physiological Time Seriescited by 0
  5. 2026Enhancing Mental Health Counseling Support in Bangladesh using Culturally-Grounded Knowledgecited by 0
  6. 2026LiFT: How to Enable In-Context Learning for Longitudinal Modellingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
  2. 2023From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
  3. 2025Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
  4. 2025Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk
  5. 2026mind_call: A Dataset for Mental Health Function Calling with Large Language Models
  6. 2026OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs
  7. 2026Analyzing LLM Reasoning to Uncover Mental Health Stigma
  8. 2025MiraMind: Benchmarking Reliable Mental Health Reasoning beyond Answer Accuracy