Supplementary Material for "Extracting Chronic Kidney Disease Comorbidities from Abstracts using Advanced Machine Learning Techniques: A Comparative Analysis"
Description
Background: Chronic Kidney Disease (CKD) is a global health concern and is frequently underdiagnosed due to its subtle initial symptoms, contributing to increasing morbidity and mortality. A comprehensive understanding of CKD comorbidities could help with identifying risk-groups, providing more efficient therapies, and increasing patient outcomes. Our study has dual aims: to establish a robust machine learning (ML) process for identifying comorbidities from abstracts, and to compile an extensive list of conditions that influence CKD's onset and progression. Methods: We analysed 39,680 abstracts with CKD in the title downloaded from the Embase library. Seven machine-learning classifiers were compared in identifying abstracts with information of a disease affecting CKD development and/or progression. The top-performing classifier was then further trained with active learning. The relevant disease names were extracted from the chosen abstracts using a novel entity relation extraction technique. The corresponding abstract of each disease was manually reviewed and a final comorbidity list was established. Findings: The SVM classifier proved to be the most effective and was selected for further active learning training. Our machine learning (ML) pipeline helped to identify 71 comorbidities across 15 ICD-10 disease groups that play a role in the onset or progression of CKD. A review of the selected abstracts revealed that certain diseases have a direct causative impact on CKD, while others, such as schizophrenia, have an indirect effect. Interpretation: These insights could steer future research into CKD by promoting the consideration of a wider range of comorbidities in CKD prognostic models. Our study ultimately boosts understanding of prognostic comorbidities and aids in clinical practice by improving patient tracking, preventative strategies, and early detection for individuals at elevated risk of CKD onset or progression. Funding: This research is a part of the first author's PhD project. No additional funding was received for this study.