Automated PRO-CTCAE Symptom Selection based on Prior Adverse Event Profiles

• URL: http://arxiv.org/abs/2512.06919v1
• Date: Sun, 07 Dec 2025 16:56:27 GMT
• Title: Automated PRO-CTCAE Symptom Selection based on Prior Adverse Event Profiles
• Abstract summary: 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.
• Score: 1.7573009491327234
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The PRO-CTCAE is an NCI-developed patient-reported outcome system for capturing symptomatic adverse events in oncology trials. It comprises a large library drawn from the CTCAE vocabulary, and item selection for a given trial is typically guided by expected toxicity profiles from prior data. Selecting too many PRO-CTCAE items can burden patients and reduce compliance, while too few may miss important safety signals. We present an automated method to select a minimal yet comprehensive PRO-CTCAE subset based on historical safety data. Each candidate PRO-CTCAE symptom term is first mapped to its corresponding MedDRA Preferred Terms (PTs), which are then encoded into Safeterm, a high-dimensional semantic space capturing clinical and contextual diversity in MedDRA terminology. 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. Spectral analysis is then applied to the combined utility and diversity matrix to identify an orthogonal set of medical concepts that balances relevance and diversity. Symptoms are rank-ordered by importance, and a cut-off is suggested based on the explained information. The tool is implemented as part of the Safeterm trial-safety app. We evaluate its performance using simulations and oncology case studies in which PRO-CTCAE was employed. This automated approach can streamline PRO-CTCAE design by leveraging MedDRA semantics and historical data, providing an objective and reproducible method to balance signal coverage against patient burden.

Related papers

• Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation [2.7998963147546143]
  This paper presents the architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation.<n>The platform introduces a decoupled framework that separates clinical perception from data orchestration while preserving traceable reasoning.
  arXiv  Detail & Related papers  (2026-07-06T10:36:04Z)
• Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel [0.5833117322405447]
  Anastomotic leak remains one of the most serious complications following colorectal cancer surgery.<n>No validated CT-based method exists to predict anastomotic leak risk prior to surgery.
  arXiv  Detail & Related papers  (2026-06-01T12:20:43Z)
• CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation [51.11942945171396]
  Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching.<n>We propose CT-FineBench, a benchmark built from CT-RATE and Merlin to evaluate the fine-grained factual consistency of CT reports.<n>Our benchmark is constructed through a meticulous, Question-Answering (QA) based process.
  arXiv  Detail & Related papers  (2026-04-27T03:32:46Z)
• 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)
• PRIMA: Pre-training with Risk-integrated Image-Metadata Alignment for Medical Diagnosis via LLM [9.229938448766484]
  PRIMA is a framework that integrates domain-specific knowledge into multi-modal representation learning.<n>Our framework achieves superior robustness without the need for massive data collection or exhaustive computational resources.
  arXiv  Detail & Related papers  (2026-02-26T18:07:52Z)
• 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)
• UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR [15.533178197005208]
  We propose a unified framework for prognostic question answering that bridges this modality gap.<n>UniPACT's core contribution is a structured prompting mechanism that converts numerical EHR data into semantically rich text.<n>We evaluate it on the comprehensive MDS-ED benchmark, it achieves a state-of-the-art mean AUROC of 89.37% across a diverse set of prognostic tasks.
  arXiv  Detail & Related papers  (2026-01-25T17:35:52Z)
• Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials [1.6733877161852464]
  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.
  arXiv  Detail & Related papers  (2025-11-24T09:42:58Z)
• CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs [14.116060944536011]
  We introduce CLaDMoP, a new pre-training approach for clinical trial outcome prediction.<n>CLaDMoP encodes trials' eligibility criteria-linked to a lightweight Drug-Molecule branch through a novel multi-level fusion technique.<n>CLaDMoP achieves up to 10.5% improvement in PR-AUC and 3.6% in ROC-AUC, while attaining comparable F1 score to MEXA-CTP.
  arXiv  Detail & Related papers  (2025-05-24T05:45:32Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [54.98321887435557]
  This paper presents a suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design.<n>We provide basic validation methods for each task to ensure the datasets' usability and reliability.<n>We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping [0.0]
  Using computational systems for the extraction of medical attributes offers many applications. BERT-based models can be used to transform clinical notes into a series of representations. We propose an alternative pipeline utilizing ScispaCyNeumann for extraction of common diseases.
  arXiv  Detail & Related papers  (2023-10-05T02:51:51Z)
• PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage [2.602118060856794]
  Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage.
  arXiv  Detail & Related papers  (2023-08-21T07:18:51Z)
• 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)
• 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)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• An efficient representation of chronological events in medical texts [9.118144540451514]
  We proposed a systematic methodology for learning from chronological events available in clinical notes. The proposed methodological it path signature framework creates a non-parametric hierarchical representation of sequential events of any type. The methodology was developed and externally validated using the largest in the UK secondary care mental health EHR data.
  arXiv  Detail & Related papers  (2020-10-16T14:54:29Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.