Evidence map›Paper›PMID 39926055›Full record

ReviewJMA journal2025

Development of Artificial Intelligence Systems for Chronic Kidney Disease.

Eiichiro Kanda

Abstract readReview
In one paragraph

Review in JMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Eiichiro KandaDepartment of Health Data Science, Kawasaki Medical School, Kukrashiki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AIcategory theorychronic kidney diseasedialysisguidelinesmachine learningnatural language processingprognosis

Identifiers

PMID39926055
PMCPMC11799718

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.