Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. Closed intervention sets also allow for a pruning strategy that is strictly more efficient than the traditional pruning strategy used by the Apriori algorithm. To implement our ideas, we introduce and compare five methods of estimating causal effect from observational data and rigorously evaluate them on synthetic data to mathematically prove (when possible) why they work. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of 152000 patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
|Original language||English (US)|
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Dec 2019|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: Dec 9 2019 → Dec 12 2019
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||12/9/19 → 12/12/19|
Bibliographical noteFunding Information:
This study is supported by National Science Foundation (NSF) grant: IIS-1602394 and National Institute of Health (NIH) grant: LM011972. Contents of this document are the sole responsibility of the authors and do not necessarily represent official views of the NSF.
© 2019 IEEE.