From Detection to Discovery: A Closed-Loop Approach for Simultaneous and Continuous Medical Knowledge Expansion and Depression Detection on Social Media

• URL: http://arxiv.org/abs/2510.23626v1
• Date: Thu, 23 Oct 2025 20:34:36 GMT
• Title: From Detection to Discovery: A Closed-Loop Approach for Simultaneous and Continuous Medical Knowledge Expansion and Depression Detection on Social Media
• Authors: Shuang Geng, Wenli Zhang, Jiaheng Xie, Rui Wang, Sudha Ram,
• Abstract summary: Social media user-generated content (UGC) provides real-time, self-reported indicators of mental health conditions such as depression.<n>We develop a framework that integrates prediction and knowledge expansion in an iterative learning cycle.
• Score: 4.322611985429637
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Social media user-generated content (UGC) provides real-time, self-reported indicators of mental health conditions such as depression, offering a valuable source for predictive analytics. While prior studies integrate medical knowledge to improve prediction accuracy, they overlook the opportunity to simultaneously expand such knowledge through predictive processes. We develop a Closed-Loop Large Language Model (LLM)-Knowledge Graph framework that integrates prediction and knowledge expansion in an iterative learning cycle. In the knowledge-aware depression detection phase, the LLM jointly performs depression detection and entity extraction, while the knowledge graph represents and weights these entities to refine prediction performance. In the knowledge refinement and expansion phase, new entities, relationships, and entity types extracted by the LLM are incorporated into the knowledge graph under expert supervision, enabling continual knowledge evolution. Using large-scale UGC, the framework enhances both predictive accuracy and medical understanding. Expert evaluations confirmed the discovery of clinically meaningful symptoms, comorbidities, and social triggers complementary to existing literature. We conceptualize and operationalize prediction-through-learning and learning-through-prediction as mutually reinforcing processes, advancing both methodological and theoretical understanding in predictive analytics. The framework demonstrates the co-evolution of computational models and domain knowledge, offering a foundation for adaptive, data-driven knowledge systems applicable to other dynamic risk monitoring contexts.

Related papers

• Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes [55.310195121276074]
  We propose a Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework to predict diseases.<n>It integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy.<n>It provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.
  arXiv  Detail & Related papers  (2025-12-09T05:37:54Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs [39.47350988195002]
  Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction.<n>We propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture.<n>Our framework consists of a linkage agent for mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions.
  arXiv  Detail & Related papers  (2025-07-03T16:35:11Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions [4.660203987415476]
  Our proposed methodology integrates medical knowledge with ICU data, improving clinical decision modeling. It combines graph representations with vital signs and clinical reports, enhancing performance. Our model includes an interpretability component to understand how knowledge graph nodes affect predictions.
  arXiv  Detail & Related papers  (2023-11-13T09:11:55Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring Systems [8.354034992258482]
  The proposed unified interpretable intelligent learning diagnosis (UIILD) framework benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI.
  arXiv  Detail & Related papers  (2022-07-07T07:04:22Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.