Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Ohara, Toshiaki; Ikeda, Hiroshi; Sugitani, Yoshiki; Suito, Hiroshi; Huynh, Viet Quang Huy; Kinomura, Masaru; Haraguchi, Soichiro; Sakurama, Kazufumi

doi:10.7150/ijms.53298

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…ML has demonstrated a pivotal role in estimating the onset of acute kidney injury [2][3][4][5] and the therapeutic responses of diabetic nephropathy [6] and IgA nephropathy [7]. In dialysis treatment, ML has also shown to be useful in the adjustment of the erythropoiesis-stimulating agent dosage for renal anemia [8][9][10][11], prediction of the occurrence of hypotension [12][13][14], and evaluation of fluid volume for patients undergoing dialysis [15]. In healthcare systems, ML has the potential to improve the selection of appropriate investigation and therapeutic processes, possibly resulting in improved prognosis in hemodialysis patients.…”

Section: Introductionmentioning

confidence: 99%

Predicting dry weight change in Hemodialysis patients using machine learning

Inoue,

Oya,

Aizawa

et al. 2023

BMC Nephrol

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Background Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient’s physical conditions. Methods All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session. Results The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward. Conclusions The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Predicting dry weight change in Hemodialysis patients using machine learning

Inoue,

Oya,

Aizawa

et al. 2023

BMC Nephrol

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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

et al. 2023

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Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a “rapid review”, since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. The review was registered on PROSPERO. Results From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice. Graphical abstract

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