Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to desig…
C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.
problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? Ho…
Machine learning improves trial analysis precision by adjusting for prognostic variables.
problem Improving precision in randomized trial analyses using covariate adjustment.
method Targeted machine learning estimation (TMLE) with adaptive pre-specification.
result Maximized empirical efficiency through cross-validated variance minimization.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
New framework for adaptive clinical trials to address real-world challenges.
problem Real-world challenges in post-regulatory clinical trials.
method RFAN framework integrating regulatory constraints and treatment policy value.
result Empirical evaluation of RFAN's performance.
Automated machine learning aims to automate the whole process of machine learning, including model configuration. In this paper, we focus on automated hyperparameter optimization (HPO) based on sequential model-based optimization (SMBO). Though conventional SMBO algorithms work well when abundant HPO trials are availab…
Efficiently find near-optimal medical treatments with less trial and error.
problem Finding effective medical treatments through trial and error.
method Formalizes the problem, uses a causal inference framework, and proposes model-based dynamic programming and greedy algorithms.
result Our methods compare favorably to model-free reinforcement learning, offering a more transparent trade-off between search time and treatment efficacy.
TrialGraph uses graph machine learning to improve clinical trial design and predict side effects.
problem Complexity and cost in clinical trials hinder drug development.
method Curated clinical trial data set converted to graph-structured formats, applied graph machine learning algorithms.
result MetaPath2Vec algorithm performed exceptionally well, improving prediction accuracy.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
problem Complex relationships between efficacy and toxicity in new drug trials.
method Adaptive clinical trial methodology that maximizes cumulative efficacy while ensuring safety constraints.
result SEEDA outperforms existing methods in finding optimal doses with higher success rates and fewer patients.
Causal ML methods failed to validate their personalized treatment effects in two large trials.
problem Validating causal machine learning methods for personalized treatment effects in precision medicine.
method Assessed 17 mainstream causal heterogeneity ML methods using two large randomized controlled trials.
result None of the ML methods reliably validated their performance, internal or external, showing significant discrepancies between training and test data.
MILCCI integrates labels across categories for better understanding of multi-trial data.
problem Understanding how labels encode multi-trial observations and disentangling their effects.
method Sparse per-trial decomposition leveraging label similarities within each category.
result MILCCI identifies interpretable components and integrates label information.
Bayesian BIC for multi-trial data improves VAR model order selection.
problem Optimal VAR model order selection for multi-trial event-based data.
method Derive and apply Bayesian Information Criterion (BIC) for multi-trial ensemble data.
result Multi-trial BIC successfully recovers real model order and estimates small model order.
Study adaptive clinical trial methods for identifying patient subpopulations with treatment benefit.
problem Adaptive identification of patient subpopulations with treatment benefit in clinical trials.
method Proposes AdaGGI and AdaGCPI meta-algorithms for subpopulation construction.
result Empirical investigation of AdaGGI and AdaGCPI performance across various simulation scenarios.
G-computation improves clinical trial power with machine learning.
problem Balancing prognostic factors in randomized trials to prevent near-confounders.
method G-computation with penalized models (Lasso, Elasticnet) and algorithm-based methods (neural network, SVM, super learner).
result G-computation with Elasticnet and splines reduces variance and increases power in RCTs.
Paper proposes a sequential statistical test for comparing imitation learning policies with near-optimal stopping.
problem Challenges in rigorously comparing imitation learning policies due to small sample sizes and potential p-hacking.
method Sequential statistical test that adapts the number of trials based on intermediate results, achieving near-optimal stopping.
result Reduces the number of evaluation trials by up to 32% compared to state-of-the-art baselines, saving significant time and effort.
Bayesian method improves clinical trial efficiency.
problem Increase treatment effect estimates in clinical trials.
method Combines prognostic covariate adjustment with a Bayesian framework.
result Substantial increase in statistical power with controlled type I error.
New algorithm improves source separation with multi-trial supervision.
problem Non-convex optimization and interpretability of independent components.
method Proximal gradient-type algorithm in invertible matrices with backpropagation for joint learning.
result Increased success rate of non-convex optimization and improved interpretability.
Proposes a new Q-learning method for survival outcomes in clinical trials.
problem Incomplete follow-up data and nonlinear covariate effects in clinical trials.
method Combines Buckley-James boosting with flexible base learners for estimating optimal treatment regimes.
result Improves treatment decision accuracy and stability in longitudinal clinical trials.
New methods improve causal inference generalization using trial and observational data.
problem Limited trial data makes generalizing causal inferences to target populations statistically infeasible.
method Develops algorithms that combine trial and observational data to estimate complex nuisance functions.
result Improves generalization of causal inferences when the additional observational study is high-quality.
Machine learning boosts RCT efficiency by controlling type I error and improving statistical power.
problem Improving statistical efficiency in RCTs with complex covariate adjustments.
method Machine learning-assisted adjustment under Rosenbaum's framework for exact tests.
result The proposed method robustly controls type I error and significantly boosts statistical efficiency.
Enhances clinical trial predictions by quantifying uncertainty.
problem Uncertainty in medical diagnosis and drug discovery predictions.
method Selective classification integrated with Hierarchical Interaction Network (HINT).
result Significant improvement in PR-AUC, F1, ROC-AUC, and overall accuracy.
Digital twins improve single-arm trials by providing robust treatment effect estimates.
problem Lack of control arms in single-arm trials limits their gold-standard evidence.
method Outcome-model-based synthetic controls using machine learning models trained on historical data.
result Digital twins offer more robust treatment effect estimates and principled corrections.
Unsupervised machine learning helps design complex experiments more efficiently.
problem Designing experiments with many factors and constraints is challenging and costly.
method Applied a beta variational autoencoder (beta-VAE) to represent trials in a low-dimensional latent space.
result Generated pragmatic designs with fewer trials while maintaining objectives.
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment ca…
Combines trial and observational data to improve policy evaluation.
problem External validity of randomized trial results in target populations.
method Uses covariate data to model trial sampling and certifies policy evaluations.
result Valid trial-based policy evaluations under model miscalibration.
Develops a TL framework for estimating RMST difference in clinical trials.
problem Estimating RMST difference in clinical trials with time-to-event outcomes.
method Targeted learning (TL) framework using pseudo-observations and copy reference (CR) approach for sensitivity analysis.
result Demonstrated the effectiveness of the TL framework using real data.
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
Hippo optimizes deep learning hyper-parameters by reducing redundant trials.
problem Redundant hyper-parameter trials in hyper-parameter optimization.
method Hippo breaks down hyper-parameter sequences into stages and executes them in a tree structure.
result Hippo reduces GPU-hours and training time significantly compared to existing methods.
Framework for estimating treatment effects using external control data.
problem Improving efficiency in estimating average treatment effects (ATE) in hybrid trials.
method Developed a formal causal inference framework based on exchangeability assumptions and graphical criteria. Proposed estimators and efficient doubly-robust methods.
result Established finite-sample performance and demonstrated application to spinal muscular atrophy trial.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.
Robots learn new tasks autonomously with minimal human intervention.
problem Lack of scalable data collection for robot learning.
method Multi-task imitation learning with autonomous data collection and one-shot generalization.
result Robots can continuously improve through autonomous data collection without reinforcement learning.
Adaptive Prespecification improves precision in randomized trials.
problem Selecting optimal covariates for precision in randomized trials.
method Adaptive Prespecification using V-fold cross-validation and influence curve-squared loss function.
result Substantial gains in precision, equivalent to 20-43% reductions in sample size for the same power.
New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.
problem Determining the best allocation of resources in clinical trials.
method Analysis of batched arm elimination designs and comparison with completely randomized trials.
result Simple adaptive designs universally and strictly dominate non-adaptive completely randomized trials for at least three treatment arms.
This paper addresses the question of how a previously available control policy πs can be used as a supervisor to more quickly and safely train a new learned control policy πL for a robot. A weighted average of the supervisor and learned policies is used during trials, with a heavier weight initially on the superv…
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
QR-learner estimates individual treatment effects using external data.
problem Limited power to detect individual treatment effects in randomized trials.
method Model-agnostic learner that estimates conditional average treatment effects (CATE) using external data.
result QR-learner reduces mean squared error and can recover true CATE.
Privacy-preserving inference for clinical trials using differential privacy.
problem Balancing knowledge sharing and privacy in healthcare data.
method Differential privacy (DP) applied to log-linear belief updates in distributed settings.
result Differentially private, distributed inference methods outperform existing techniques.
The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant possible applications in adaptive clinical trials, which allow for dynamic changes in the treatment allocation probabilities of patients. However, most bandit learning algorithms are d…
Machine learning improves learning and memory retention by optimizing study sessions.
problem Improving learning and memory retention methods for factual material.
method Large-scale randomized controlled trial with machine learning optimization of study sessions.
result Study sessions optimized with machine learning lead to 67% longer retention and 50% higher return rate.
Most of machine learning deals with vector parameters. Ideally we would like to take higher order information into account and make use of matrix or even tensor parameters. However the resulting algorithms are usually inefficient. Here we address on-line learning with matrix parameters. It is often easy to obtain onlin…
An app-based mHealth intervention uses reinforcement learning to send effective reminder notifications.
problem Designing an effective reinforcement learning algorithm for app-based mHealth interventions.
method Developed a reinforcement learning algorithm to send reminder notifications based on participant likelihood of app engagement.
result The algorithm improved app-based mHealth interventions by reducing participant burden and promoting behavior change.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
problem Inconclusive results in TOPCAT trial for heart failure treatment.
method Causal discovery methods with domain knowledge integration.
result Significant causal effects shown for some subgroups globally.
Improves pre-trial risk assessments by making them safer without changing existing rules.
problem Improving pre-trial risk assessments while maintaining deterministic rules.
method Developed a maximin robust optimization approach to find a safer policy.
result Can safely improve certain components of the risk assessment instrument.
New method uses latent variables to estimate treatment effects from single-arm trials.
problem Estimating treatment effects from single-arm trials due to lack of external control groups.
method Latent-variable modeling with amortized variational inference for patient matching and direct effect estimation.
result Improved performance in direct treatment effect estimation and effect estimation via patient matching compared to previous methods.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
problem Limited covariates in small clinical trials reduce the effectiveness of analysis.
method Leverage historical data to pre-specify covariate weights as a composite covariate.
result A composite covariate improves the cost/benefit ratio and reduces overfitting in small clinical trials.
New methods improve subgroup analysis in trials with limited data.
problem Limited sample sizes in subgroup analyses of randomized controlled trials.
method Two TMLEs that borrow information from non-subgroup participants.
result Improved precision in subgroup-specific treatment effect estimates.