Applications of machine learning methods in kidney disease: hope or hype?
Purpose Of Review: The universal adoption of electronic health records, improvement in technology, and the availability of continuous monitoring has generated large quantities of healthcare data. Machine learning is increasingly adopted by nephrology researchers to analyze this data in order to impr...
| Publicado en: | Current Opinion in Nephrology & Hypertension Vol. 29; no. 3; pp. 319 - 327 |
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| Autores principales: | , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144216542&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144216542 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10624821 IQP jtl: Current Opinion in Nephrology & Hypertension issn: 10624821 maglogo: N pubinfo: dt: May2020 vid: 29 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 144216542 144216542 NLM32235273 144216542 10.1097/MNH.0000000000000604 NLM32235273 144216542 ppf: 319 ppct: 8 formats: tig: atl: Applications of machine learning methods in kidney disease: hope or hype? aug: au: Chan, Lili Vaid, Akhil Nadkarni, Girish N. affil: Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA sug: subj: Kidney Diseases Therapy Algorithms Research, Medical Natural Language Processing ab: Purpose Of Review: The universal adoption of electronic health records, improvement in technology, and the availability of continuous monitoring has generated large quantities of healthcare data. Machine learning is increasingly adopted by nephrology researchers to analyze this data in order to improve the care of their patients.Recent Findings: In this review, we provide a broad overview of the different types of machine learning algorithms currently available and how researchers have applied these methods in nephrology research. Current applications have included prediction of acute kidney injury and chronic kidney disease along with progression of kidney disease. Researchers have demonstrated the ability of machine learning to read kidney biopsy samples, identify patient outcomes from unstructured data, and identify subtypes in complex diseases. We end with a discussion on the ethics and potential pitfalls of machine learning.Summary: Machine learning provides researchers with the ability to analyze data that were previously inaccessible. While still burgeoning, several studies show promising results, which will enable researchers to perform larger scale studies and clinicians the ability to provide more personalized care. However, we must ensure that implementation aids providers and does not lead to harm to patients. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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