A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations

• URL: http://arxiv.org/abs/2507.22919v1
• Date: Mon, 21 Jul 2025 07:56:06 GMT
• Title: A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations
• Authors: Qixuan Hu, Xumou Zhang, Jinman Kim, Florence Bourgeois, Adam G. Dunn,
• Abstract summary: We evaluated methods for predicting serious adverse event results in clinical trials using information only from their registrations prior to the trial.<n>We analysed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results.
• Score: 8.134674449860668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objectives: With accurate estimates of expected safety results, clinical trials could be designed to avoid terminations and limit exposing participants to unnecessary risks. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analysed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results. Two prediction models were developed: a classifier predicting will experimental arm have higher SAE rates (area under the receiver operating characteristic curve; AUC) than control arm, and a regression model to predict the proportion of SAEs in control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with downstream model for prediction. To maintain semantic representation in long trial texts exceeding localised language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC predicting which trial arm has a higher proportion of patients with SAEs. When predicting proportion of participants experiencing SAE in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed methods without it. Across 12 classifiers, the average absolute AUC increase was 2.00%; across 12 regressors, the average absolute RMSE reduction was 1.58%. Discussion: Summary results data available at ClinicalTrials.gov remains underutilised. The potential to estimate results of trials before they start is an opportunity to improve trial design and flag discrepancies between expected and reported safety results.

Related papers

• A Data-Driven Approach to Support Clinical Renal Replacement Therapy [1.7666791716676549]
  This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Replacement Therapy (CRRT)<n>Using time-series data from an ICU, 16 clinically selected features were identified to train predictive models.<n>Results remained robust across different forecasting horizons.
  arXiv  Detail & Related papers  (2026-02-26T11:47:22Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• Case Prompting to Mitigate Large Language Model Bias for ICU Mortality Prediction [17.91443453604627]
  Large language models (LLMs) show promise in predicting outcomes from structured medical data.<n>LLMs may exhibit demographic biases related to sex, age, and race, limiting their trustworthy use in clinical practice.<n>We propose a training-free, clinically adaptive prompting framework to simultaneously improve fairness and performance.
  arXiv  Detail & Related papers  (2025-12-17T12:29:53Z)
• Methodology for Comparing Machine Learning Algorithms for Survival Analysis [55.65997641180011]
  Six machine learning models for survival analysis were evaluated.<n>XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532, followed by GBSA and RSF)
  arXiv  Detail & Related papers  (2025-10-28T14:42:28Z)
• Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences [56.23412698865433]
  We focus on Prediction-Powered Causal Inferences (PPCI)<n> PPCI estimates the treatment effect in a target experiment with unlabeled factual outcomes, retrievable zero-shot from a pre-trained model.<n>We validate our method on synthetic and real-world scientific data, offering solutions to instances not solvable by vanilla Empirical Risk Minimization.
  arXiv  Detail & Related papers  (2025-02-10T10:52:17Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Advanced Meta-Ensemble Machine Learning Models for Early and Accurate Sepsis Prediction to Improve Patient Outcomes [0.0]
  This paper examines the limitations of traditional sepsis screening tools like Systemic Inflammatory Response Syndrome, Modified Early Warning Score, and Quick Sequential Organ Failure Assessment. We propose using machine learning techniques - Random Forest, Extreme Gradient Boosting, and Decision Tree models - to predict sepsis onset. Our study evaluates these models individually and in a combined meta-ensemble approach using key metrics such as Accuracy, Precision, Recall, F1 score, and Area Under the Receiver Operating Characteristic Curve.
  arXiv  Detail & Related papers  (2024-07-11T00:51:32Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction [0.5249805590164902]
  The choice of the most effective treatment may eventually be influenced by breast cancer survival prediction. In this study, 1904 patient records were utilized to predict a 5-year breast cancer survival using a machine learning approach.
  arXiv  Detail & Related papers  (2023-04-13T12:32:14Z)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning [67.8195828626489]
  Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. We propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates.
  arXiv  Detail & Related papers  (2023-04-07T23:04:27Z)
• The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards [0.0]
  We evaluate how framing affects model performance and model learning in four different approaches. We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities.
  arXiv  Detail & Related papers  (2021-01-26T14:00:05Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data [5.844828229178025]
  Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission.
  arXiv  Detail & Related papers  (2020-11-18T15:56:28Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation [5.050648346154715]
  Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
  arXiv  Detail & Related papers  (2020-01-08T10:04:42Z)

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.