Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials

• URL: http://arxiv.org/abs/2511.18937v1
• Date: Mon, 24 Nov 2025 09:42:58 GMT
• Title: Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials
• Abstract summary: We present a knowledge-based method for reviewing treatment-emergent adverse events (AEs) in clinical trials.<n>The approach enhances MedDRA by adding a hidden medical knowledge layer (Safeterm) that captures semantic relationships between terms in a 2-D map.
• Score: 1.6733877161852464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a graphical, knowledge-based method for reviewing treatment-emergent adverse events (AEs) in clinical trials. The approach enhances MedDRA by adding a hidden medical knowledge layer (Safeterm) that captures semantic relationships between terms in a 2-D map. Using this layer, AE Preferred Terms can be regrouped automatically into similarity clusters, and their association to the trial disease may be quantified. The Safeterm map is available online and connected to aggregated AE incidence tables from ClinicalTrials.gov. For signal detection, we compute treatment-specific disproportionality metrics using shrinkage incidence ratios. Cluster-level EBGM values are then derived through precision-weighted aggregation. Two visual outputs support interpretation: a semantic map showing AE incidence and an expectedness-versus-disproportionality plot for rapid signal detection. Applied to three legacy trials, the automated method clearly recovers all expected safety signals. Overall, augmenting MedDRA with a medical knowledge layer improves clarity, efficiency, and accuracy in AE interpretation for clinical trials.

Related papers

• Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis [47.559337485629904]
  This work introduces MedVCR, a counterfactual reasoning framework that mimics clinical diagnostic thinking.<n> MedVCR comprises three components: a Counterfactual Generator that synthesizes tissue evolution under specified pathological states.<n>It is evaluated under both fully supervised (e.g., colposcopy) and weakly supervised (e.g., colonoscopy) video diagnosis settings, yielding 2.6%-10.2% performance gains.
  arXiv  Detail & Related papers  (2026-05-26T02:46:15Z)
• Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity [66.94391219005291]
  This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic.<n>Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling.<n>ACP achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines.
  arXiv  Detail & Related papers  (2026-03-18T06:15:35Z)
• MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation [1.055294253870608]
  Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis.<n>Recent CLIP-based studies are promising for anomaly detection in zero-/few-shot settings.<n>We study supervised adaptation of CLIP for MAD under a realistic clinical setting.
  arXiv  Detail & Related papers  (2026-03-18T03:37:44Z)
• Using Unsupervised Domain Adaptation Semantic Segmentation for Pulmonary Embolism Detection in Computed Tomography Pulmonary Angiogram (CTPA) Images [0.0]
  unsupervised domain adaptation (UDA) framework is proposed, utilizing a Transformer backbone and a Mean-Teacher architecture for cross-center semantic segmentation.<n>The primary focus is placed on enhancing pseudo-label reliability by learning deep structural information within the feature space.<n> Experimental validation conducted on cross-center datasets (FUMPE and CAD-PE) demonstrates significant performance gains.
  arXiv  Detail & Related papers  (2026-02-23T14:33:24Z)
• SHIELD: Semantic Heterogeneity Integrated Embedding for Latent Discovery in Clinical Trial Safety Signals [1.8620638885220202]
  SHIELD is a novel methodology for automated and integrated safety signal detection in clinical trials.<n>It combines disproportionality analysis with semantic clustering of adverse event terms applied to MedDRA term embeddings.<n>Resulting clusters are annotated with syndrome-level summary labels using large language models.
  arXiv  Detail & Related papers  (2026-02-23T13:55:36Z)
• Automated PRO-CTCAE Symptom Selection based on Prior Adverse Event Profiles [1.7573009491327234]
  We present an automated method to select a minimal yet comprehensive PRO-CTCAE subset based on historical safety data.<n>We score each candidate PRO item for relevance to the historical list of adverse event PTs and combine relevance and incidence into a utility function.<n>This approach can streamline PRO-CTCAE design by leveraging MedDRA semantics and historical data.
  arXiv  Detail & Related papers  (2025-12-07T16:56:27Z)
• Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning [2.6279333406008476]
  We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA)<n>Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration.
  arXiv  Detail & Related papers  (2025-09-22T12:57:21Z)
• Explainable AI (XAI) for Arrhythmia detection from electrocardiograms [0.0]
  Deep learning has enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption.<n>This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis.
  arXiv  Detail & Related papers  (2025-08-24T10:44:24Z)
• Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation [45.96892342675963]
  We propose an Uncertainty-aware Cross-training framework for semi-supervised medical image (UC-Seg)<n>Our method achieves superior segmentation accuracy and generalization performance compared to other state-of-the-art semi-supervised methods.
  arXiv  Detail & Related papers  (2025-08-12T15:28:10Z)
• Automated Measurement of Optic Nerve Sheath Diameter Using Ocular Ultrasound Video [14.016658180958444]
  This paper presents a novel method to automatically identify the optimal frame from video sequences for ONSD measurement.<n>The proposed method achieved a mean error, mean squared deviation, and intraclass correlation coefficient (ICC) of 0.04, 0.054, and 0.782, respectively.
  arXiv  Detail & Related papers  (2025-06-03T12:14:51Z)
• Revisiting Medical Image Retrieval via Knowledge Consolidation [46.6989555659494]
  We propose a novel method to consolidate knowledge of hierarchical features and functions.<n>We introduce Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH)<n>Our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.
  arXiv  Detail & Related papers  (2025-03-12T13:16:42Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree [2.924868086534434]
  We present an automatic binary classification framework for brain signals in multichannel EEG recordings. We propose a novel method to reduce the dimension of the feature space without compromising the quality of the extracted features. CatBoost achieves the binary classification accuracy of 87.68%, and outperforms state-of-the-art techniques on the same dataset.
  arXiv  Detail & Related papers  (2020-12-18T03:36:52Z)
• Representation learning for improved interpretability and classification accuracy of clinical factors from EEG [7.323779456638996]
  EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their clinical utility has not been fully realized because of 1) the lack of automated ways to deal with the inherent noise associated with EEG data at scale, and 2) the lack of knowledge of which aspects of the EEG signal may be markers of a clinical disorder.
  arXiv  Detail & Related papers  (2020-10-28T23:21:36Z)

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.