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.
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
problem Personalizing health interventions for dyadic relationships in mobile health.
method Dyadic Reinforcement Learning (dyadic RL), a Bayesian and hierarchical online algorithm.
result Established a regret bound and demonstrated empirical performance through simulations and real data.
Develops algorithms to balance personalization and statistical power in mobile health studies.
problem Balancing personalization and statistical power in mobile health studies.
method Develops general meta-algorithms to modify existing bandit algorithms.
result Guarantees sufficient power while improving user well-being.
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…
In many mobile health interventions, treatments should only be delivered in a particular context, for example when a user is currently stressed, walking or sedentary. Even in an optimal context, concerns about user burden can restrict which treatments are sent. To diffuse the treatment delivery over times when a user i…
BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.
problem Adapting to complex, non-linear user behaviors in personalized mobile health interventions.
method Bayesian Forest Thompson Sampling (BFTS) integrates Bayesian Additive Regression Trees (BART) into the exploration loop of contextual bandits.
result BFTS achieves state-of-the-art regret on tabular benchmarks and improves engagement rates by over 30% in a behavioral intervention study.
GAMBITTS uses GenAI for adaptive interventions, improving decision-making.
problem Adaptive interventions with GenAI-generated content.
method Generator-mediated bandit-Thompson sampling (GAMBITTS).
result GAMBITTS outperforms standard bandit methods in mobile health interventions.
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses real-time data collection and communication capabilities of modern mobile devices to deliver…
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals. Yet the high amount of noise due to the in situ delivery of mHealth interventions can cripple the ability of an algorithm to learn …
Personalized models using group attributes reduce performance, study finds.
problem Reducing performance of models using group attributes like race or gender.
method Formal conditions and collective preference guarantees to ensure fair use.
result Models personalized with group attributes reduce performance at a group level.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
We propose a flexible method for estimating value functions in reinforcement learning without parametric assumptions.
problem Lack of interpretability in reinforcement learning models, especially in healthcare applications.
method Nonparametric additive model using local kernel regression and basis expansion.
result Personalized, adaptive recommendations for postoperative recovery.
In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose…
New algorithm balances personalization and statistical validity in MRTs.
problem Optimizing decisions in nonstationary settings with habituation and recovery.
method ROGUE-TS Thompson Sampling with probability clipping.
result Achieves lower regret and maintains high statistical power.
Study assesses whether RL algorithm personalizes treatment sequences.
problem Evaluate if RL algorithm truly personalizes treatment sequences.
method Resampling-based methodology to investigate personalization.
result RL algorithm's personalization may be due to stochasticity.
RoME optimizes mobile health interventions by modeling user and time-specific effects.
problem Challenges in optimizing mobile health interventions due to participant heterogeneity, nonstationarity, and nonlinear relationships.
method RoME uses a Robust Mixed-Effects contextual bandit algorithm with random effects, network cohesion penalties, and debiased machine learning.
result RoME achieves robust regret bounds even with complex baseline rewards, demonstrating superior performance in simulations and studies.
Study reveals LLM personality patterns but lacks behavioral consistency.
problem Understanding and validating personality traits in LLMs.
method Characterized LLM personality across three dimensions: training dynamics, self-report validity, and intervention effects.
result Self-reported traits do not reliably predict behavior, and instructional alignment affects trait expression but not behavior.
Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy lev…
RL platform enhances user journeys in healthcare apps.
problem Improving user experience and personalization in healthcare apps.
method Reinforcement learning framework for adaptive interventions.
result Significant increase in basket size through personalized recommendations.
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.
Inferring a person's goal from their behavior is an important problem in applications of AI (e.g. automated assistants, recommender systems). The workhorse model for this task is the rational actor model - this amounts to assuming that people have stable reward functions, discount the future exponentially, and construc…
Improved assessment of knee osteoarthritis using geodesic B-score.
problem Need for automatic, reader-independent measures of osteoarthritis clinical outcomes.
method Derive a geodesic B-score for Riemannian shape spaces, develop efficient algorithm for large shape populations.
result Geodesic B-score exhibits improved discrimination ability over Euclidean B-score.
Footfall based biometric system is perhaps the only person identification technique which does not hinder the natural movement of an individual. This is a clear edge over all other biometric systems which require a formidable amount of human intervention and encroach upon an individual's privacy to some extent or the o…
Study user engagement in mobile health apps for health workers in resource-poor settings.
problem Detect churn and tailor content for health workers in mobile health apps.
method Probabilistic and survival analysis of behavioral logs.
result Personalized measures of meaningful engagement can enhance health outcomes.
Reduces variance in noisy social outcomes to improve policy evaluation and optimization.
problem Improving access to opportunity through personalized treatment decisions.
method Data-driven dimensionality-reduction using reduced rank regression to denoise multiple outcomes.
result Improves estimation error in policy evaluation and optimization, including on real-world data.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
problem Uncertainty quantification in continuous treatments for personalized healthcare decisions.
method Causal dose-response problem framed as covariate shift, using weighted conformal prediction with propensity estimation and kernel functions.
result Demonstrates the significance of covariate shift assumptions for robust prediction intervals.
Personalized stress model using transfer learning from 20 participants.
problem Limited generalizability of machine learning models due to individual physiological differences.
method Transfer learning from a base model trained on 20 participants' physiological data collected in real-time.
result Improved model personalization and cross-domain performance.
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
problem How human advisors use context to guide interventions in algorithm-assisted advising.
method Mixed-methods approach combining quantitative and qualitative data from a randomized controlled trial.
result 2 out of 3 interventions by advisors were plausibly 'expertly targeted' to students using non-algorithmic context.
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
Develops c-GNF for personalized social science policy analysis.
problem Challenges in estimating causal effects and counterfactual inference in social sciences.
method causal-Graphical Normalizing Flow (c-GNF) method.
result c-GNF performs well in estimating causal effects and counterfactual inference.
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.
The paper proposes a method to find subgroups with significant treatment effects in noisy data.
problem Estimating the causal effects of interventions on noisy outcomes.
method A machine-learning method specifically optimized for finding subgroups with significant effects, designed to maximize the probability of obtaining a statistically significant positive treatment effect.
result The proposed method yields higher power in detecting subgroups affected by the treatment compared to standard tree-based tools.
FOCaL meta-learner estimates functional treatment effects robustly.
problem Estimating heterogeneous treatment effects from functional outcomes.
method Doubly robust meta-learner FOCaL integrating functional regression.
result Direct and robust estimation of F-CATE.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
This paper develops explainable treatment policies for RPM using clinical knowledge.
problem Barriers to adoption of DHIs and lack of interpretability in purely black-box algorithms.
method Developed a pipeline for learning explainable treatment policies using clinician-informed representations.
result Policies learned from clinician-informed representations are more efficacious and efficient than black-box policies.
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…
In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspect…
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time an…
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…
Proposes pT-Learning for optimal dynamic treatment regimes in mHealth.
problem Challenges in learning optimal dynamic treatment regimes with large intervention options and infinite time horizon.
method Proximal Temporal consistency Learning (pT-Learning) framework for adaptively adjusting between deterministic and stochastic policies.
result Minimax estimator avoids double sampling issue and can incorporate off-policy data.
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…
Estimates joint causal effects using single-variable interventions on nonlinear models.
problem Estimating joint causal effects from single-variable interventions.
method Identifiability result and practical estimator for decomposing causal effects.
result Joint effects can be inferred without joint interventional data for nonlinear additive models.
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.
This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.
problem CRL under unknown multi-node interventions, focusing on single-node assumptions.
method Establishes identifiability results for general latent causal models under stochastic interventions.
result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
problem Discovering the true causal graph from observational data with limited interventions.
method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.
result Provides approximation algorithms with competitive ratios for verification and search problems.
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
Developed a new algorithm to improve dynamic treatment regimens.
problem Non-convergence of Q-learning-based Q-shared algorithm in dynamic treatment regimens.
method Penalized Q-shared algorithm to address convergence issues.
result The penalized Q-shared algorithm converges and outperforms the original in various settings.