From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Clinical decision making needs to be supported by evidence that treatments are beneficial to
individual patients. Although randomized control trials (RCTs) are the gold standard for …
individual patients. Although randomized control trials (RCTs) are the gold standard for …
Causal transformer for estimating counterfactual outcomes
V Melnychuk, D Frauen… - … Conference on Machine …, 2022 - proceedings.mlr.press
Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …
Recent updates on innovative approaches to overcome drug resistance for better outcomes in cancer
The multi-dimensional challenge of drug resistance is one of the pivotal hindrances for
cancer chemotherapy. A reductive approach to define and distinguish the main aspects of …
cancer chemotherapy. A reductive approach to define and distinguish the main aspects of …
Forecasting treatment responses over time using recurrent marginal structural networks
B Lim - Advances in neural information processing systems, 2018 - proceedings.neurips.cc
Electronic health records provide a rich source of data for machine learning methods to
learn dynamic treatment responses over time. However, any direct estimation is hampered …
learn dynamic treatment responses over time. However, any direct estimation is hampered …
Continuous-time modeling of counterfactual outcomes using neural controlled differential equations
Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …
Active observing in continuous-time control
The control of continuous-time environments while actively deciding when to take costly
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for
estimating treatment effects from longitudinal observational data assume that there are no …
estimating treatment effects from longitudinal observational data assume that there are no …
Accounting for informative sampling when learning to forecast treatment outcomes over time
T Vanderschueren, A Curth… - International …, 2023 - proceedings.mlr.press
Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …
outcomes over time, which could ultimately enable the adoption of more individualized …
G-net: a recurrent network approach to g-computation for counterfactual prediction under a dynamic treatment regime
R Li, S Hu, M Lu, Y Utsumi… - … Learning for Health, 2021 - proceedings.mlr.press
Counterfactual prediction is a fundamental task in decision-making. This paper introduces G-
Net, a sequential deep learning framework for counterfactual prediction under dynamic time …
Net, a sequential deep learning framework for counterfactual prediction under dynamic time …
Bayesian neural controlled differential equations for treatment effect estimation
Treatment effect estimation in continuous time is crucial for personalized medicine.
However, existing methods for this task are limited to point estimates of the potential …
However, existing methods for this task are limited to point estimates of the potential …