Complex Dynamics in Psychological Data: Mapping Individual Symptom Trajectories to Group-Level Patterns

• URL: http://arxiv.org/abs/2507.14161v1
• Date: Mon, 07 Jul 2025 16:38:37 GMT
• Title: Complex Dynamics in Psychological Data: Mapping Individual Symptom Trajectories to Group-Level Patterns
• Authors: Eleonora Vitanza, Pietro DeLellis, Chiara Mocenni, Manuel Ruiz Marin,
• Abstract summary: This study integrates causal inference, graph analysis, temporal complexity measures, and machine learning to examine whether individual symptom trajectories can reveal meaningful diagnostic patterns.<n> testing on a longitudinal dataset of N=45 individuals affected by General Anxiety Disorder (GAD) and/or Major Depressive Disorder (MDD) derived from Fisher et al.<n>New dataset yields 91% accuracy in the classification of the symptom dynamics, proving to be an effective diagnostic support tool.
• Score: 0.18749305679160366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study integrates causal inference, graph analysis, temporal complexity measures, and machine learning to examine whether individual symptom trajectories can reveal meaningful diagnostic patterns. Testing on a longitudinal dataset of N=45 individuals affected by General Anxiety Disorder (GAD) and/or Major Depressive Disorder (MDD) derived from Fisher et al. 2017, we propose a novel pipeline for the analysis of the temporal dynamics of psychopathological symptoms. First, we employ the PCMCI+ algorithm with nonparametric independence test to determine the causal network of nonlinear dependencies between symptoms in individuals with different mental disorders. We found that the PCMCI+ effectively highlights the individual peculiarities of each symptom network, which could be leveraged towards personalized therapies. At the same time, aggregating the networks by diagnosis sheds light to disorder-specific causal mechanisms, in agreement with previous psychopathological literature. Then, we enrich the dataset by computing complexity-based measures (e.g. entropy, fractal dimension, recurrence) from the symptom time series, and feed it to a suitably selected machine learning algorithm to aid the diagnosis of each individual. The new dataset yields 91% accuracy in the classification of the symptom dynamics, proving to be an effective diagnostic support tool. Overall, these findings highlight how integrating causal modeling and temporal complexity can enhance diagnostic differentiation, offering a principled, data-driven foundation for both personalized assessment in clinical psychology and structural advances in psychological research.

Related papers

• A Retrieval-Augmented Multi-Agent Framework for Psychiatry Diagnosis [44.4032296111169]
  MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
  arXiv  Detail & Related papers  (2025-06-04T09:18:25Z)
• Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction [45.89562183034469]
  Existing deep learning diagnosis prediction models with intrinsic interpretability often assign attention weights to every past diagnosis or hospital visit.<n>We introduce SHy, a self-explaining hypergraph neural network model, designed to offer personalized, concise and faithful explanations.<n> SHy captures higher-order disease interactions and extracts distinct temporal phenotypes as personalized explanations.
  arXiv  Detail & Related papers  (2025-02-15T06:33:02Z)
• Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia [0.9297614330263184]
  This study investigates the potential of multimodal data integration to diagnose mental diseases like schizophrenia, depression, and anxiety.<n>Using Apache Spark and convolutional neural networks (CNNs), a data-driven classification pipeline has been developed for big data environment.<n>The importance of coherence features is highlighted by comparative analysis, which shows significant improvement in classification accuracy and robustness.
  arXiv  Detail & Related papers  (2025-02-06T10:30:13Z)
• Diagnosis driven Anomaly Detection for CPS [44.97616703648182]
  We propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD) We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-27T15:34:40Z)
• Stroke recovery phenotyping through network trajectory approaches and graph neural networks [0.966840768820136]
  We analyze data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes.
  arXiv  Detail & Related papers  (2021-09-29T18:46:08Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts [20.879437896802408]
  We introduce an intelligent visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's AI-augmented interface guides the user through an organized and holistic analysis space. We conduct several case studies using real data from the research database of Parkinson's Progression Markers Initiative.
  arXiv  Detail & Related papers  (2020-10-13T06:34:45Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.