Deep learning predicts response to HER2-targeted breast cancer therapy.
problem Predicting response to HER2-targeted neoadjuvant chemotherapy.
method Developed and validated a deep learning approach using pre-treatment dynamic breast MRI.
result Deep learning model achieved strong performance in predicting pathological complete response.
CAST models time-varying treatment effects in cancer patients.
problem Estimating treatment effects at fixed time points limits understanding of dynamic changes over time.
method CAST combines parametric and non-parametric methods to model continuous time-varying treatment effects.
result CAST reveals how treatment effects rise, peak, and decline over the follow-up period.
Reinforcement learning algorithms are gaining popularity in fields in which optimal scheduling is important, and oncology is not an exception. The complex and uncertain dynamics of cancer limit the performance of traditional model-based scheduling strategies like Optimal Control. Motivated by the recent success of mode…
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous po…
Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
problem Chemotherapy pharmacokinetics (PK) with tissue concentration not measured
method Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
result PINN recovers tissue concentration and identifies non-identifiable parameters
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based o…
New methods use RL and DA to improve dosing precision and reduce side effects.
problem Improving precision and safety of anticancer drug dosing.
method Bayesian data assimilation and reinforcement learning.
result Significantly reduces incidence of severe side effects.
Study uses machine learning to predict heart failure in cancer patients.
problem Early detection of cancer patients at risk for cardiotoxicity.
method Examined four machine learning algorithms on 143,199 cancer patients.
result Gradient boosting model achieved best AUC score of 0.9077.
DSL estimates heterogeneous treatment effects over time in survival settings.
problem Complicated by right censoring and time-varying treatment effects.
method Deep survival learner (DSL) for estimating CATEs over a clinically relevant time spectrum.
result DSL reveals heterogeneity in perioperative chemotherapy effects over time.
End-to-end pipeline for data-driven decision making in mixed-integer optimization.
problem Data-driven decision making in mixed-integer optimization with uncertainty.
method Exploiting mixed-integer optimization-representability of machine learning methods, characterizing decision trust regions, and ensembling multiple models.
result Framework generates high-quality prescriptions and controls model robustness.
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.