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Kidney Week

ASN / Education & Meetings / Kidney Week /
Abstract: FR-PO1096

Natural Language Processing Artificial Intelligence (AI) Predicts CKD Progression in Medical-Word Virtual Space

Session Information

Category: CKD (Non-Dialysis)

  • 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Kanda, Eiichiro, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
  • Epureanu, Bogdan I., University of Michigan, Ann Arbor, Michigan, United States
  • Adachi, Taiji, Kyoto Daigaku, Kyoto, Japan
  • Sasaki, Tamaki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
  • Kashihara, Naoki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
Background

Chronic kidney disease (CKD) leads to end-stage renal disease (ESRD) or death. A new surrogate marker reflecting its pathophysiology has been needed for CKD therapy.

Methods

In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory.

Results

A virtual space of medical words was constructed from the CKD-related literature (n=165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied the definition of vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n=26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group.

Conclusion

This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.