Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
- Conference Paper
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electronic health records, a great variety of data has been collected in clinical practice, enabling the evaluation of treatments and treatment policies based on observational data. In this paper, we focus on learning individualized treatment rules (ITRs) to derive a treatment policy that is expected to generate a better outcome for an individual patient. In our framework, we cast ITRs learning as a contextual bandit problem and minimize the expected risk of the treatment policy. We conduct experiments with the proposed framework both in a simulation study and based on a real-world dataset. In the latter case, we apply our proposed method to learn the optimal ITRs for the administration of intravenous (IV) fluids and vasopressors (VP). Based on various offline evaluation methods, we could show that the policy derived in our framework demonstrates better performance compared to both the physicians and other baselines, including a simple treatment prediction approach. As a long-Term goal, our derived policy might eventually lead to better clinical guidelines for the administration of IV and VP. Mehr anzeigen
Buchtitel2020 IEEE International Conference on Healthcare Informatics (ICHI)
Seiten / Artikelnummer
ThemaIndividualized treatment rules; Contextual bandit problem; Off-policy learning
AnmerkungenDue to the Coronavirus (COVID-19) the conference was conducted virtually.