arXiv research

A recommendation framework helps users choose healthcare interventions.

Unified RL survey for healthcare AI interventions.

New bandit model for healthcare intervention planning.

AI improves precision health through adaptive interventions.

Paper improves feature selection for predicting outcomes from observational data.

NCoRE learns counterfactual representations for combined treatments.

Combines adversarial and interventional robustness for machine learning models.

RL platform enhances user journeys in healthcare apps.

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital f…

New framework infers causal shifts in event sequences under out-of-domain interventions.

It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered …

Study user engagement in mobile health apps for health workers in resource-poor settings.

Study finds incorporating fairness in healthcare models doesn't improve performance or net benefit.

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…

Simulation framework assesses ROI of chronic disease adherence and policy timing.

Neural TPPs improve EHR modelling efficiency.

Develops a framework for causal structure learning using both interventional and observational data.

New approach tackles non-Markovian behavior in maternal health programs.

Paper proposes a new multi-task causal Gaussian process model for better prediction and uncertainty estimation.

Optimizes data labeling for causal effect estimation with missing outcomes.

The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.

New monitoring method detects ML risk models' performance changes in medical interventions.

Framework identifies comorbidities for frequent ED and inpatient visits.

Optimizes causal effects on unknown graphs using Causal Entropy Optimization.

Optimizes risk assessment tools using mixed-integer programming.

Proposes a method for generating prediction intervals in dose-response models using conformal prediction.

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…

Causal thinking improves healthcare decisions from EHRs.

We propose a flexible method for estimating value functions in reinforcement learning without parametric assumptions.

New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.

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…

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria f…

Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, bett…

New method estimates policy performance under unobserved confounding.

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated pictur…

Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.

ReRe detects anomalies in real-time for time series data.

Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist i…

Probabilistic ML improves healthcare data analysis.

The paper develops a method to optimize individualized treatment rules for cost-effectiveness.

A statistical description and model of individual healthcare expenditures in the US has been developed for measuring value in healthcare. We find evidence that healthcare expenditures are quantifiable as an infusion-diffusion process, which can be thought of intuitively as a steady change in the intensity of treatment …

Big data analytics improves healthcare through early detection and quality life.

Estimates joint causal effects using single-variable interventions on nonlinear models.

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

This paper provides a ML framework for diabetes prediction and care management.

In this paper, we aimed at reviewing several different approaches present today in the search for more accurate diagnostic and treatment management in mental healthcare. Our focus is on mood disorders, and in particular on the major depressive disorder (MDD). We are reviewing and discussing findings based on neuroimagi…

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…