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  1. Article ; Online: Introduction to Artificial Intelligence and Machine Learning in Nephrology.

    Nadkarni, Girish N

    Clinical journal of the American Society of Nephrology : CJASN

    2022  Volume 18, Issue 3, Page(s) 392–393

    MeSH term(s) Humans ; Artificial Intelligence ; Nephrology ; Machine Learning ; Algorithms
    Language English
    Publishing date 2022-01-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2226665-3
    ISSN 1555-905X ; 1555-9041
    ISSN (online) 1555-905X
    ISSN 1555-9041
    DOI 10.2215/CJN.0000000000000068
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6584: Show issues Location:
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  2. Article ; Online: Advances in Chronic Kidney Disease Lead Editorial Outlining the Future of Artificial Intelligence/Machine Learning in Nephrology.

    Kotanko, Peter / Nadkarni, Girish N

    Advances in kidney disease and health

    2023  Volume 30, Issue 1, Page(s) 2–3

    MeSH term(s) Humans ; Artificial Intelligence ; Nephrology ; Machine Learning ; Forecasting ; Renal Insufficiency, Chronic
    Language English
    Publishing date 2023-01-31
    Publishing country United States
    Document type Editorial
    ZDB-ID 3156601-7
    ISSN 2949-8139
    ISSN (online) 2949-8139
    DOI 10.1053/j.akdh.2022.11.008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4627: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  3. Article ; Online: Clinical Informatics in Critical Care Medicine.

    Nadkarni, Girish N / Sakhuja, Ankit

    The Yale journal of biology and medicine

    2023  Volume 96, Issue 3, Page(s) 397–405

    Abstract: Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these ... ...

    Abstract Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.
    MeSH term(s) Humans ; Algorithms ; Critical Care ; Critical Illness ; Intensive Care Units ; Medical Informatics
    Language English
    Publishing date 2023-09-29
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 200515-3
    ISSN 1551-4056 ; 0044-0086
    ISSN (online) 1551-4056
    ISSN 0044-0086
    DOI 10.59249/WTTU3055
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  4. Article ; Online: Federated Learning in Health care Using Structured Medical Data.

    Oh, Wonsuk / Nadkarni, Girish N

    Advances in kidney disease and health

    2023  Volume 30, Issue 1, Page(s) 4–16

    Abstract: The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way ...

    Abstract The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.
    MeSH term(s) Humans ; Health Facilities ; Academies and Institutes ; Commerce ; Information Dissemination ; Delivery of Health Care
    Language English
    Publishing date 2023-01-28
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 3156601-7
    ISSN 2949-8139
    ISSN (online) 2949-8139
    DOI 10.1053/j.akdh.2022.11.007
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4627: Show issues Location:
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  5. Article ; Online: The Future of Artificial Intelligence and Machine Learning in Kidney Health and Disease.

    Nadkarni, Girish N / Kotanko, Peter

    Advances in chronic kidney disease

    2022  Volume 29, Issue 5, Page(s) 425–426

    MeSH term(s) Algorithms ; Artificial Intelligence ; Forecasting ; Humans ; Kidney ; Machine Learning
    Language English
    Publishing date 2022-10-17
    Publishing country United States
    Document type Editorial ; Research Support, Non-U.S. Gov't
    ISSN 1548-5609 ; 1548-5595
    ISSN (online) 1548-5609
    ISSN 1548-5595
    DOI 10.1053/j.ackd.2022.09.001
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  6. Article ; Online: Federated Learning in Risk Prediction: A Primer and Application to COVID-19-Associated Acute Kidney Injury.

    Gulamali, Faris F / Nadkarni, Girish N

    Nephron

    2022  Volume 147, Issue 1, Page(s) 52–56

    Abstract: Background: Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns.: Summary: Federated learning provides a ... ...

    Abstract Background: Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns.
    Summary: Federated learning provides a functional alternative to the single-institution approach while avoiding the pitfalls of data sharing. In cross-silo federated learning, the data do not leave a site. The raw data are stored at the site of collection. Models are created at the site of collection and are updated locally to achieve a learning objective. We demonstrate a use case with COVID-19-associated AKI. We showed that federated models outperformed their local counterparts, even when evaluated on local data in the test dataset, and performance was like those being used for pooled data. Increases in performance at a given hospital were inversely proportional to dataset size at a given hospital, which suggests that hospitals with smaller datasets have significant room for growth with federated learning approaches.
    Key messages: This short article provides an overview of federated learning, gives a use case for COVID-19-associated acute kidney injury, and finally details the issues along with some potential solutions.
    MeSH term(s) Humans ; COVID-19/complications ; Algorithms ; Health Facilities ; Hospitals ; Acute Kidney Injury/etiology
    Language English
    Publishing date 2022-07-14
    Publishing country Switzerland
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 207121-6
    ISSN 2235-3186 ; 1423-0186 ; 1660-8151 ; 0028-2766
    ISSN (online) 2235-3186 ; 1423-0186
    ISSN 1660-8151 ; 0028-2766
    DOI 10.1159/000525645
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 169: Show issues Location:
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  7. Article ; Online: Proteomic Analyses Unveil Actionable Disease Pathways in COVID-19: A Step Toward Targeted Therapies.

    Vasquez-Rios, George / Nadkarni, Girish N

    JACC. Basic to translational science

    2022  Volume 7, Issue 5, Page(s) 442–444

    Language English
    Publishing date 2022-05-23
    Publishing country United States
    Document type Editorial ; Comment
    ISSN 2452-302X
    ISSN (online) 2452-302X
    DOI 10.1016/j.jacbts.2022.03.011
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  8. Article ; Online: Penetrance of Deleterious Clinical Variants-Reply.

    Forrest, Iain S / Nadkarni, Girish N / Do, Ron

    JAMA

    2022  Volume 327, Issue 19, Page(s) 1927

    MeSH term(s) Genetic Predisposition to Disease/genetics ; Humans ; Mutation/genetics ; Penetrance ; Phenotype
    Language English
    Publishing date 2022-06-23
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2022.4634
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  9. Article ; Online: The authors reply.

    Paranjpe, Ishan / Nadkarni, Girish N

    Kidney international

    2020  Volume 98, Issue 5, Page(s) 1347–1348

    MeSH term(s) Humans ; Urinary Calculi
    Language English
    Publishing date 2020-10-30
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 120573-0
    ISSN 1523-1755 ; 0085-2538
    ISSN (online) 1523-1755
    ISSN 0085-2538
    DOI 10.1016/j.kint.2020.08.017
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  10. Article: An ounce of public health for COVID-19?

    Nadkarni, Girish N.

    Science Translational Medicine

    Abstract: Multifaceted nonpharmaceutical interventions supported by data restricted spread of COVID-19 in ... ...

    Abstract Multifaceted nonpharmaceutical interventions supported by data restricted spread of COVID-19 in China
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #152266
    Database COVID19

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