Personalized medicine has become an important part of medicine, for instance predicting individual drug responses based on genomic information. However, many current statistical methods are not tailored to this task, because they overlook the individual heterogeneity of patients. In this paper, we look at personalized …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
Trend · papers per month
48 results for “Personalized Medicine”
AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunitiesstat.ME
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Private RL algorithm with privacy guarantees for personalized medicine decisions.
problem Privacy-preserving reinforcement learning for personalized medicine decisions.
method Developed a private optimism-based RL algorithm using joint differential privacy (JDP).
result Achieved strong PAC and regret bounds with a privacy guarantee.
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumorscs.CL
Study uses LLMs to create personalized treatment plans for rare gynecological tumors.
problem Suboptimal management and poor prognosis due to low incidence and heterogeneity of rare gynecological tumors.
method Developed a digital twin system using LLMs to integrate clinical and biomarker data.
result LLM-enabled digital twins efficiently model individual patient trajectories and identify potential treatment options.
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.
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
Paper proposes federated offline RL for personalized medicine.
problem Privacy constraints and heterogeneity in healthcare data.
method Multi-site Markov decision process model and first federated policy optimization algorithm.
result The proposed algorithm achieves comparable suboptimality to centralized RL.
Proposes a method to learn from historical data for personalized decision-making.
problem Sample hunger in sequential decision-making algorithms for personalized medicine.
method Identifiable latent bandit framework using nonlinear independent component analysis.
result Optimal decision-making with shorter exploration time than classical bandits.
CAPITAL algorithm identifies optimal patient subgroups for better treatment.
problem Identify maximum number of patients benefiting from better treatment.
method Constrained Policy Tree Search (CAPITAL) algorithm to find optimal subgroup selection rule (SSR).
result Maximizes the number of patients with enhanced treatment effects.
Paper uses stats to predict treatment choice based on illness probability.
problem Improving treatment decision-making in personalized medicine.
method Statistical decision theory with maximum regret evaluation.
result Estimates illness probability for better treatment choice.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
problem Estimating heterogeneous treatment effects and uplift modeling.
method Unified survey of treatment effect heterogeneity and uplift modeling approaches.
result Unified notations for comparing methods and applications in personalized marketing, medicine, and social studies.
Develops methods to learn optimal treatment regimes using causal tree methods.
problem Lack of methods for estimating treatment effects and handling complex patient data.
method Causal tree and causal forest methods for estimating heterogeneous treatment effects.
result Outperforms state-of-the-art baselines in cumulative regret and percentage of optimal decisions.
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimesstat.ME
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Statistical test evaluates if personalizing interventions is cost-effective.
problem Balancing the benefits of personalizing interventions with their potential costs.
method Developed a statistical hypothesis test to assess the performance of personalized interventions.
result The test shows that personalized interventions can outperform standard approaches under certain conditions.
Generative models create personalized patient health simulations.
problem Creating accurate digital twins for personalized medicine.
method Neural network architecture for conditional generative models of clinical trajectories.
result Same architecture generates accurate twins across 13 indications.
Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curvemath.ST
Estimates personalized treatment response curves using covariates.
problem Flexible estimation of personalized treatment response curves.
method Sieve based nonparametric estimator of smoothed regimen-response curve function.
result Asymptotic linearity and undersmoothing criteria for efficient estimation.
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Predictionstat.ML
The paper proposes a method to estimate heterogeneous treatment effects using pretraining strategies.
problem Estimating conditional average treatment effects (CATE) in the presence of many covariates.
method The approach leverages prognostic factors that also predict treatment effect heterogeneity, using the R-learner framework.
result The proposed method improves estimation accuracy and power for detecting treatment effect heterogeneity.
Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.
problem Gene mutation prediction in hepatocellular carcinoma for personalized treatments.
method Multi-instance multi-label learning with oversampling for data imbalance.
result Proposed approach shows superiority in gene mutation prediction.
Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of per…
Modeling correlated mutations in cancer for personalized treatment.
problem Identifying mutations for personalized cancer therapy in heterogeneous profiles.
method Proposed correlated zero-inflated negative binomial process with mixed beta-Bernoulli and variational inference.
result Identified biologically relevant correlations between somatic mutations.
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researc…
DeepCoDA provides personalized interpretability for complex health data.
problem Interpreting complex health data, especially compositional data, is challenging.
method DeepCoDA framework for high-dimensional compositional data, personalized interpretability through patient-specific weights.
result DeepCoDA maintains state-of-the-art performance and provides coherent, personalized interpretations.
New meta-learners estimate time-varying treatment effects without model assumptions.
problem Estimating treatment effects over time in personalized medicine.
method Model-agnostic meta-learners for weighted pseudo-outcome regressions.
result Comprehensive theoretical analysis and practical insights for choosing meta-learners.
Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important. Currently, data can be collected at the microbiome level leading to the possibility of personalized medicine whereby tr…
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
problem Finding patient subgroups with similar characteristics for personalized decision-making.
method Bayesian Supervised Causal Clustering (BSCC) that identifies homogenous subgroups based on treatment effects.
result BSCC identifies subgroups with similar covariate profiles and treatment effects.
New methods optimize personalized treatment assignment in trials with many arms.
problem Poor performance of standard methods in trials with many treatment arms.
method Regularized and clustered joint assignment forest algorithm.
result Gains in predicting arm-wise outcomes and utility gains from personalization.
Personalized models explain TB treatment outcomes considering patient context.
problem Heterogeneity in TB treatment outcomes due to co-morbidities.
method Multi-task learning approach encoding patient context into personalized models.
result Identifies anemia, age of onset, and HIV as influential for treatment efficacy.
The paper introduces algorithms for uncertainty quantification in metric spaces.
problem Uncertainty quantification in regression models defined on metric spaces.
method Proposes conformal and kNN prediction algorithms for metric spaces.
result Both algorithms provide finite-sample guarantees and improve local coverage calibration.
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and the clustered structure of biomedical data together interact to present a joint challenge in the hi…
We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The standa…
SAFER improves personalized treatment recommendations for dynamic clinical contexts.
problem Personalized treatment optimization in evolving clinical contexts with safety concerns.
method Integrates structured EHR and clinical notes, uses conformal prediction for safe recommendations.
result SAFER outperforms state-of-the-art baselines in recommendation metrics and mortality rates.
Estimates price sensitivity from transaction data using a novel odds ratio method.
problem Estimate price sensitivity from transaction-level data with partially observed treatment assignments.
method Recursive partitioning procedure with adversarial imputation for robust estimation.
result Validated on synthetic data and applied to three case studies, demonstrating heterogeneity in treatment effects.
New method identifies important features and interactions in RF models.
problem Limited theoretical understanding of local feature and interaction importance in RF models.
method Combines global and local analysis to identify frequent feature co-occurrences.
result Proves consistent recovery of true local signal features and interactions.
New method improves CATE estimation in low overlap regions.
problem Low overlap in CATE estimation leads to poor performance of meta-learners.
method Overlap-Adaptive Regularization (OAR) that regularizes models proportionally to overlap weights.
result OAR significantly improves CATE estimation in low-overlap settings.
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Agestat.ML
New methods for selecting variables in complex biomedical data.
problem Selecting important variables in multivariate, functional, and complex biomedical data.
method Optimization-based variable selection methods for various regression models.
result Outperforms state-of-the-art methods in accuracy and speed.
Develops a Causal Transformer for estimating counterfactual outcomes from longitudinal data.
problem Estimating counterfactual outcomes over time from observational data is challenging due to complex, long-range dependencies.
method Combines three transformer subnetworks with separate inputs for time-varying covariates, previous treatments, and previous outcomes into a joint network with in-between cross-attentions. Uses a custom, end-to-end training procedure with a counterfactual domain confusion loss to address confounding bias.
result Achieves superior performance over current baselines in synthetic and real-world datasets.
GEAR uses auxiliary data to estimate optimal decisions in studies with limited primary outcomes.
problem Estimating optimal decisions when primary outcomes are not available in experimental samples.
method GEAR uses augmented inverse propensity weighting to estimate optimal decisions based on auxiliary data.
result GEAR estimators and value estimators have established asymptotic properties and are validated in simulations and a real application.
Proposes DSW for unbiased ITE estimation with dynamic confounders.
problem Estimating ITE from dynamic observational data with time-varying confounders.
method Deep Sequential Weighting (DSW) infers hidden confounders using current treatment assignments and historical information.
result DSW generates unbiased and accurate treatment effects.
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender…
Proposes efficient data acquisition for personalized treatment effects from observational data.
problem Efficiently acquiring outcomes for personalized treatment effects in observational studies.
method Introduces causal, Bayesian acquisition functions to select points with overlapping support.
result Demonstrates improved sample efficiency and accuracy in learning personalized treatment effects.
New algorithms protect user data while optimizing personalized decisions.
problem Personalized decision-making with private user data.
method Developed LDP algorithms for stochastic generalized linear bandits using SGD and OLS.
result Achieved the same regret bound as non-privacy settings with LDP.
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient po…
Develops a method to estimate personalized treatment regimes from summary statistics.
problem Estimating optimal treatment regimes for a target population when individual-level data is unavailable.
method A weighting framework that tailors a treatment regime for the target population using summary statistics.
result Consistent and asymptotically normal estimator for optimal treatment regimes.
A new algorithm learns optimal personalized treatment plans online with low regret.
problem Learning optimal dynamic treatment regimes in an online setting.
method Developed a novel algorithm balancing exploration and exploitation for rate-optimal regret.
result Guaranteed rate-optimal regret for linear transition and reward models.
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and t…
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive…
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processescs.LG
Study batch reinforcement learning methods for personalized medical treatments.
problem Batch reinforcement learning for personalized medical treatments.
method Direct policy learning and model-based learning approaches.
result Model-based learning is impossible with finite model classes but feasible with relaxed conditions.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regressioneess.SP
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
problem Challenges in interpreting HRV measurements due to variability and stressors.
method PhysioMTL combines Optimal Transport and MTL to learn personalized diurnal rhythms from heterogeneous data.
result PhysioMTL outperforms other methods in predicting unseen subjects and generating counterfactual effects.