Continuous Treatment Recommendation with Deep Survival Dose Response Function

• URL: http://arxiv.org/abs/2108.10453v5
• Date: Tue, 26 Sep 2023 05:49:02 GMT
• Title: Continuous Treatment Recommendation with Deep Survival Dose Response Function
• Authors: Jie Zhu, Blanca Gallego
• Abstract summary: We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data. The estimated treatment effect from DeepSDRF enables us to develop recommender algorithms with the correction for selection bias. This is the first time that causal models are used to address the continuous treatment effect with observational data in a medical context.
• Score: 3.705291460388999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (DeepSDRF). That is, we consider the problem of learning the conditional average dose response (CADR) function solely from historical data in which observed factors (confounders) affect both observed treatment and time-to-event outcomes. The estimated treatment effect from DeepSDRF enables us to develop recommender algorithms with the correction for selection bias. We compared two recommender approaches based on random search and reinforcement learning and found similar performance in terms of patient outcome. We tested the DeepSDRF and the corresponding recommender on extensive simulation studies and the eICU Research Institute (eRI) database. To the best of our knowledge, this is the first time that causal models are used to address the continuous treatment effect with observational data in a medical context.

Related papers

• Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data [7.308241944759317]
  An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. We propose a doubly robust estimator of the value function, and the optimal ITR is learned by maximizing the value function within a pre-specified class of ITRs. We evaluate the empirical performance of the proposed method by simulation studies and a real data application of sodium bicarbonate therapy for patients with severe metabolic acidaemia.
  arXiv  Detail & Related papers  (2023-01-13T11:47:10Z)
• Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach [0.0]
  Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process. We develop Reinforcement Learning methods to efficiently learn optimal treatment regimes.
  arXiv  Detail & Related papers  (2021-12-08T20:22:04Z)
• Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time [71.30985926640659]
  We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
  arXiv  Detail & Related papers  (2021-12-07T16:40:28Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Estimating heterogeneous survival treatment effect in observational data using machine learning [9.951103976634407]
  Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics.
  arXiv  Detail & Related papers  (2020-08-17T01:02:14Z)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.