Inferring actual treatment pathways from patient records

Adrian Wilkins-Caruana; Madhushi Bandara; Katarzyna Musial; Daniel Catchpoole; Paul J Kennedy

doi:10.1016/j.jbi.2023.104554

Inferring actual treatment pathways from patient records

J Biomed Inform. 2023 Dec:148:104554. doi: 10.1016/j.jbi.2023.104554. Epub 2023 Nov 23.

Authors

Adrian Wilkins-Caruana¹, Madhushi Bandara², Katarzyna Musial³, Daniel Catchpoole⁴, Paul J Kennedy⁵

Affiliations

¹ Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia. Electronic address: adrian.caruana@uts.edu.au.
² Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
³ Complex Adaptive Systems Lab, Data Science Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
⁴ Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Biospecimen Research Services, The Children's Cancer Research Unit, The Children's Hospital at Westmead, Australia.
⁵ Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Joint Research Centre in AI for Health and Wellness, University of Technology Sydney, Australia and Ontario Tech University, Canada.

PMID: 38000767
DOI: 10.1016/j.jbi.2023.104554

Abstract

Objective: Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. The objective of this study is to develop a method for inferring actual treatment steps for a particular patient group from administrative health records - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research.

Methods: We introduce Defrag, a method for examining health records to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability, establish benchmarks, and compare against baselines.

Results: We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag inference method, and to compare Defrag to several baselines, where it significantly outperforms non-NN-based methods.

Conclusions: Defrag offers an innovative and effective approach for inferring treatment pathways from complex health data. Defrag significantly outperforms several existing pathway-inference methods, but computationally-derived treatment pathways are still difficult to compare against clinical guidelines. Furthermore, the open-source code for Defrag and the testing framework are provided to encourage further research in this area.

Keywords: Clinical pathway; Electronic health records; Healthcare data; Neural networks; Pathway inference; Treatment pathway.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Breast Neoplasms*
Electronic Health Records*
Female
Humans