Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation

• URL: http://arxiv.org/abs/2505.16044v2
• Date: Wed, 04 Jun 2025 15:35:08 GMT
• Title: Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation
• Abstract summary: This study shifts the focus to individual symptom estimation using a multimodal approach.<n>We develop unimodal models for each modality and a multimodal framework to improve accuracy and severity.
• Score: 4.599023238114995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shifts the focus to individual symptom severity estimation using a multimodal approach that integrates speech, video, and text inputs. We develop unimodal models for each modality and a multimodal framework to improve accuracy and robustness. By capturing a more detailed symptom profile, this approach can help in enhancing diagnostic precision and support personalized treatment, offering a scalable and objective tool for mental health assessment.

Related papers

• SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis [71.98702207668346]
  Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making.<n>This paper addresses the problem of self-explainable multimodal diagnosis by formulating it within the information bottleneck (IB) framework.<n>We propose a unified learning paradigm that jointly optimize predictive performance and modality-specific explainability.
  arXiv  Detail & Related papers  (2026-09-04T14:13:24Z)
• A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data [36.77792803657935]
  We argue that several important barriers to adoption can be addressed using Bayesian network modelling.<n>We evaluate a model for depression and anxiety symptom prediction from voice and speech features in large-scale datasets.
  arXiv  Detail & Related papers  (2025-12-08T17:28:09Z)
• Quantifying Articulatory Coordination as a Biomarker for Schizophrenia [3.9389809100079614]
  Schizophrenia is a complex disorder with diverse symptoms including disorganized speech and social withdrawal.<n>We present an interpretable framework that leverages articulatory speech features to quantify vocal tract coordination.<n>This approach offers a transparent, severity-sensitive biomarker for schizophrenia, advancing the potential for clinically interpretable speech-based assessment tools.
  arXiv  Detail & Related papers  (2025-11-05T00:19:05Z)
• Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
  One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
  arXiv  Detail & Related papers  (2025-10-02T19:38:46Z)
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
  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)
• Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions [0.9297614330263184]
  An advanced multimodal deep learning system for the automated classification of PTSD and depression is presented in this paper.<n>The proposed method achieves classification accuracies of 92% for depression and 93% for PTSD, outperforming traditional unimodal approaches.
  arXiv  Detail & Related papers  (2025-02-06T10:57:10Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis [84.6348989654916]
  We propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process. It integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path.
  arXiv  Detail & Related papers  (2023-11-26T14:42:31Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder [0.0]
  We create a multimodal decision system based on recordings of the patient in acoustic, linguistic, and visual modalities. We achieve a 64.8% unweighted average recall score, which improves the state-of-the-art performance achieved on this dataset.
  arXiv  Detail & Related papers  (2021-12-17T12:09:01Z)
• MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis [71.2022403915147]
  We introduce MEDUSA, a multi-scale encoder-decoder self-attention mechanism tailored for medical image analysis. We obtain state-of-the-art performance on challenging medical image analysis benchmarks including COVIDx, RSNA RICORD, and RSNA Pneumonia Challenge.
  arXiv  Detail & Related papers  (2021-10-12T15:05:15Z)
• Meta-learning on Spectral Images of Electroencephalogram of Schizophenics [0.0]
  Schizophrenia is a complex psychiatric disorder involving changes in thought patterns, perception, mood, and behavior. Advances in neuroimaging and machine learning algorithms can facilitate the diagnosis of schizophrenia.
  arXiv  Detail & Related papers  (2021-01-27T20:51:25Z)
• 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)
• Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments [6.505217121471555]
  For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed measurements. We propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual.
  arXiv  Detail & Related papers  (2020-10-30T20:30:31Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)

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.