Review Article: Artificial Intelligence in Medicine Development of Artificial Intelligence Systems for Chronic Kidney Disease

被引:0
作者
Kanda, Eiichiro [1 ]
机构
[1] Kawasaki Med Sch, Dept Hlth Data Sci, Kukrashiki, Japan
来源
JMA JOURNAL | 2024年
基金
日本学术振兴会;
关键词
machine learning; prognosis; chronic kidney disease; dialysis; AI; natural language processing; category theory; guidelines;
D O I
暂无
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Chronic kidney disease (CKD) is a complex disease that is related not only to dialysis but also to the onset of cardiovascular disease and life prognosis. As renal function declines with age and depending on lifestyle, the number of patients with CKD is rapidly increasing in Japan. Accurate prognosis prediction for patients with CKD in clinical settings is important for selecting treatment methods and screening patients with high-risk. In recent years, big databases on CKD and dialysis have been constructed through the use of data science technology, and the pathology of CKD is being elucidated. Therefore, we developed an artificial intelligence (AI) system that can accurately predict the prognosis of CKD such as its progression, the timing of dialysis introduction, and death. Aiming for its social implementation, the prognosis prediction system developed for patients with CKD was released on the website. We then developed a clinical practice guideline creation support system called Doctor K as an AI system. When creating clinical practice guidelines, huge amounts of manpower and time are required to conduct a systematic review of thousands of papers. Therefore, we developed a natural language processing (NLP) AI system to significantly improve work efficiency. Doctor K was used in the preparation of the guidelines of the Japanese Society of Nephrology. Furthermore, by comparing and analyzing the medical word virtual space constructed by the NLP AI system based on patient big data, we proved using the latest mathematical theory (category theory) that this system reflects the pathology of CKD. This suggests the possibility that the NLP AI system can predict patient prognosis. We hope that, through these studies, the use of AI based on big data will lead to the development of new treatments and improvement in patient prognosis.
引用
收藏
页数:9
相关论文
共 25 条
[1]  
2023, Arxiv, DOI arXiv:2303.08774
[2]   Integrating Experiential and Distributional Data to Learn Semantic Representations [J].
Andrews, Mark ;
Vigliocco, Gabriella ;
Vinson, David .
PSYCHOLOGICAL REVIEW, 2009, 116 (03) :463-498
[3]   SWIFT-Review: A text-mining workbench for systematic review [J].
Howard B.E. ;
Phillips J. ;
Miller K. ;
Tandon A. ;
Mav D. ;
Shah M.R. ;
Holmgren S. ;
Pelch K.E. ;
Walker V. ;
Rooney A.A. ;
Macleod M. ;
Shah R.R. ;
Thayer K. .
Systematic Reviews, 5 (1)
[4]  
Japanese Society of Dialysis Medicine, 2020, Current status of chronic dialysis therapy in Japan, end of 2019
[5]  
Japanese Society of Nephrology, 2022, The second five-year plans for JSN
[6]  
Japanese Society of Nephrology, 2023, Evidence-based CKD treatment guidelines 2023
[7]   New marker for chronic kidney disease progression and mortality in medical-word virtual space [J].
Kanda, Eiichiro ;
Epureanu, Bogdan, I ;
Adachi, Taiji ;
Sasaki, Tamaki ;
Kashihara, Naoki .
SCIENTIFIC REPORTS, 2024, 14 (01)
[8]   Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality [J].
Kanda, Eiichiro ;
Epureanu, Bogdan Iuliu ;
Adachi, Taiji ;
Kashihara, Naoki .
PLOS DIGITAL HEALTH, 2023, 2 (01)
[9]   Clinical impact of suboptimal RAASi therapy following an episode of hyperkalemia [J].
Kanda, Eiichiro ;
Rastogi, Anjay ;
Murohara, Toyoaki ;
Lesen, Eva ;
Agiro, Abiy ;
Arnold, Matthew ;
Chen, Gengshi ;
Yajima, Toshitaka ;
Jarbrink, Krister ;
Pollack, Charles V. V. .
BMC NEPHROLOGY, 2023, 24 (01)
[10]   Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients [J].
Kanda, Eiichiro ;
Suzuki, Atsushi ;
Makino, Masaki ;
Tsubota, Hiroo ;
Kanemata, Satomi ;
Shirakawa, Koichi ;
Yajima, Toshitaka .
SCIENTIFIC REPORTS, 2022, 12 (01)