Skip to main content

Towards Causal Discovery on EHR data

Time: Mon 2022-08-22 10.00 - 11.00

Video link:

Respondent: Pontus Olausson , Reglerteknik/DCS

Opponent: Tobias Höppe

Supervisor: Stefan Bauer

Examiner: Cristian Rojas

Export to calendar

Abstract of the thesis: Causal discovery is the problem of learning causal relationships between variables from a set of data. One interesting area of use for causal discovery is the health care domain, where application could help facilitate a better understanding of disease and treatment mechanisms. The health care domain has recently undergone a major digitization, making available a large amount of data for use in learning algorithms, available in formats such as medical images or EHRs. This thesis aims to explore the application of causal discovery on EHR data. We provide an overview of the field of causal discovery and identify 3 contemporary methods for causal discovery on time-series data which we apply on a preprocessed version of the MIMIC-IV data set. Each causal discovery method is run on time-series comprising of EHR data related to hospital stays for patients with sepsis. We provide an empiric report of the overlap between the learned graphs from different hospital stays as a heuristic evaluation measure. We find that it is possible to identify common themes in the learned graphs between different causal discovery methods, indicating potential practical value of causal discovery on EHR data. We also identify important considerations for future application and evaluation, such as incorporating extensive domain knowledge, and provide suggestions for future work.

Page responsible:Web editors at EECS
Belongs to: Decision and Control Systems
Last changed: Aug 22, 2022