Optimal personalised treatment computation through in silico clinical trials on patient digital twins

• URL: http://arxiv.org/abs/2106.10684v1
• Date: Sun, 20 Jun 2021 12:12:36 GMT
• Title: Optimal personalised treatment computation through in silico clinical trials on patient digital twins
• Abstract summary: We present methods and an algorithm that, by means of extensive computer simulation--based experimental campaigns (ISTC) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine) e show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In Silico Clinical Trials (ISTC), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation--based experimental campaigns (ISTC) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). e show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.

Related papers

• Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation [2.6593327311591115]
  We present an online adaptive framework that integrates Treatment Effect (TE) estimation to quantify clinical benefits, a patient Digital Twin (DT) to simulate treatment trajectories, and Reinforcement Learning (RL) for sequential decision-making.<n>We validate our framework using both a synthetic clinical simulator and a real-world ovarian cancer dataset from The Cancer Genome Atlas (TCGA).<n>In both simulated and clinical settings, our method demonstrated superior effectiveness and stability in recommending treatments compared to standard computational baselines.
  arXiv  Detail & Related papers  (2026-06-16T01:39:55Z)
• Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer [0.45835414225547183]
  Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes.<n>Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials.<n>Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method.<n>Performance was evaluated using synthetic data and applied to clinical trials examining the effectiveness of panitumumab to treat metastatic colorectal cancer.
  arXiv  Detail & Related papers  (2026-02-28T18:00:26Z)
• Predicting effect of novel treatments using molecular pathways and real-world data [0.0]
  We propose a flexible and modular machine learning-based approach for predicting the efficacy of an untested pharmaceutical for treating a disease.<n>We train a machine learning model using sets of pharmaceutical-pathway weight impact scores and patient data.<n>The resulting model then analyses weighted impact scores of an untested pharmaceutical across human biological molecule-protein pathways to generate a predicted efficacy value.
  arXiv  Detail & Related papers  (2025-09-08T20:35:15Z)
• Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates [1.7099366779394252]
  Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase.<n>We propose a novel deep learning-based method to address this critical challenge.<n>We show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial.
  arXiv  Detail & Related papers  (2025-07-31T14:47:16Z)
• AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents [47.640779069547534]
  AutoCT is a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning.<n>We show that AutoCT performs on par with or better than SOTA methods on clinical trial prediction tasks within only a limited number of self-refinement iterations.
  arXiv  Detail & Related papers  (2025-06-04T11:50:55Z)
• Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care [0.0]
  Deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformerbased models, to stratify patients. Predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates.
  arXiv  Detail & Related papers  (2024-12-09T23:20:08Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• TrialSynth: Generation of Synthetic Sequential Clinical Trial Data [21.799655542003677]
  Variational Autoencoder (VAE) designed to address challenges of generating synthetic time-sequence clinical trial data. Our experiments demonstrate that Trial Synth surpasses the performance of other comparable methods.
  arXiv  Detail & Related papers  (2024-09-11T08:20:30Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. 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)
• TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction [19.084936647082632]
  We propose TrialDura, a machine learning-based method that estimates the duration of clinical trials using multimodal data. We encode them into Bio-BERT embeddings specifically tuned for biomedical contexts to provide a deeper and more relevant semantic understanding. Our proposed model demonstrated superior performance with a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years compared to the other models.
  arXiv  Detail & Related papers  (2024-04-20T02:12:59Z)
• Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges [0.250217476520467]
  This paper examines the current challenges faced in clinical trial design and optimization. The focus is on three critical aspects: clinical trial simulations, site selection, and cohort identification.
  arXiv  Detail & Related papers  (2024-04-19T18:00:25Z)
• Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm [15.627870862369784]
  Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis. We propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services.
  arXiv  Detail & Related papers  (2024-03-25T06:17:54Z)
• Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling [2.711804338865226]
  This research introduces an approach leveraging state-of-the-art Natural Language Processing (NLP) techniques. Our methodology enhances efficiency and effectiveness, significantly reducing training time without compromising performance.
  arXiv  Detail & Related papers  (2024-01-18T05:17:18Z)
• Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
  We operationalize a safe and interpretable framework to identify optimal treatment regimes. Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
  arXiv  Detail & Related papers  (2023-10-23T19:59:10Z)
• 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)
• Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints [84.09488581365484]
  Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds becomes more complex. Most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability.
  arXiv  Detail & Related papers  (2020-06-09T03:06:45Z)
• Contextual Constrained Learning for Dose-Finding Clinical Trials [102.8283665750281]
  C3T-Budget is a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints. It recruits patients with consideration of the remaining budget, the remaining time, and the characteristics of each group.
  arXiv  Detail & Related papers  (2020-01-08T11:46:48Z)

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.