Deakin University
Browse

File(s) under permanent embargo

LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment

Version 2 2024-06-06, 02:45
Version 1 2018-10-03, 08:55
journal contribution
posted on 2024-06-06, 02:45 authored by Dang NguyenDang Nguyen, Wei LuoWei Luo, D Phung, Svetha VenkateshSvetha Venkatesh
© 2018 Elsevier B.V. Cancer is a worldwide problem and one of the leading causes of death. Increasing prevalence of cancer, particularly in developing countries, demands better understandings of the effectiveness and adverse consequences of different cancer treatment regimes in real patient populations. Current understandings of cancer treatment toxicities are often derived from either “clean” patient cohorts or coarse population statistics. Thus, it is difficult to get up-to-date and local assessments of treatment toxicities for specific cancer centers. To address these problems, we propose a novel and efficient method for discovering toxicity progression patterns in the form of temporal association rules (TARs). A temporal association rule is defined as a rule where the diagnosis codes in the right hand side (e.g., a combination of toxicities/complications) are temporally occurred after the diagnosis codes in the left hand side (e.g., a particular type of cancer treatment). Our method develops a lattice structure to efficiently discover TARs. More specifically, the lattice structure is first constructed to store all frequent diagnosis codes in the dataset. It is then traversed using the paternity relations among nodes to generate TARs. Our extensive experiments show the effectiveness of the proposed method in discovering major toxicity patterns in comparison with the temporal comorbidity analysis. In addition, our method significantly outperforms existing methods for mining TARs in terms of runtime.

History

Journal

Knowledge-Based Systems

Volume

161

Pagination

313-328

Location

Amsterdam, The Netherlands

ISSN

0950-7051

eISSN

1872-7409

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, Elsevier B.V.

Publisher

ELSEVIER SCIENCE BV