Deep learning algorithms for predicting renal replacement therapy initiation in CKD patients: a retrospective cohort study.

Journal: BMC nephrology

PMID: 38486160

Abstract

BACKGROUND: Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations.

Authors

Ka-Chun Leung

Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Wincy Wing-Sze Ng

Adult Intensive Care Unit, Queen Mary Hospital, Hong Kong, China.
Yui-Pong Siu

Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, China.
Anthony Kai-Ching Hau

Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, China.
Hoi-Kan Lee

Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, China.

Keywords

Algorithms Deep Learning Disease Progression Glomerular Filtration Rate Humans Renal Dialysis Renal Insufficiency, Chronic Renal Replacement Therapy Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38486160)

Deep learning algorithms for predicting renal replacement therapy initiation in CKD patients: a retrospective cohort study.

Abstract

Authors

Keywords

External Resources

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