Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge

Jianqiu Zhang; Paul E. Drawz; Gyorgy Simon; Terrence J. Adam; Genevieve B. Melton

doi:10.3233/SHTI230959

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge

Jianqiu Zhang, Paul E. Drawz, Gyorgy Simon, Terrence J. Adam, Genevieve B. Melton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

Original language	English (US)
Title of host publication	MEDINFO 2023 - The Future is Accessible
Subtitle of host publication	Proceedings of the 19th World Congress on Medical and Health Informatics
Editors	Jen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
Publisher	IOS Press BV
Pages	219-223
Number of pages	5
ISBN (Electronic)	9781643684567
DOIs	https://doi.org/10.3233/SHTI230959
State	Published - Jan 25 2024
Event	19th World Congress on Medical and Health Informatics, MedInfo 2023 - Sydney, Australia Duration: Jul 8 2023 → Jul 12 2023

Publication series

Name	Studies in Health Technology and Informatics
Volume	310
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	19th World Congress on Medical and Health Informatics, MedInfo 2023
Country/Territory	Australia
City	Sydney
Period	7/8/23 → 7/12/23

Bibliographical note

Publisher Copyright:
© 2024 International Medical Informatics Association (IMIA) and IOS Press.

Keywords

Recurrent AKI
risk prediction

PubMed: MeSH publication types

Journal Article

Publisher link

10.3233/SHTI230959

Find It

Find It at U of M Libraries

Cite this

Zhang, J., Drawz, P. E., Simon, G., Adam, T. J., & Melton, G. B. (2024). Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics (pp. 219-223). (Studies in Health Technology and Informatics; Vol. 310). IOS Press BV. https://doi.org/10.3233/SHTI230959

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge. / Zhang, Jianqiu; Drawz, Paul E.; Simon, Gyorgy et al.
MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. ed. / Jen Bichel-Findlay; Paula Otero; Philip Scott; Elaine Huesing. IOS Press BV, 2024. p. 219-223 (Studies in Health Technology and Informatics; Vol. 310).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, J, Drawz, PE , Simon, G , Adam, TJ & Melton, GB 2024, Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge. in J Bichel-Findlay, P Otero, P Scott & E Huesing (eds), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 310, IOS Press BV, pp. 219-223, 19th World Congress on Medical and Health Informatics, MedInfo 2023, Sydney, Australia, 7/8/23. https://doi.org/10.3233/SHTI230959

Zhang J, Drawz PE , Simon G , Adam TJ , Melton GB. Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge. In Bichel-Findlay J, Otero P, Scott P, Huesing E, editors, MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. IOS Press BV. 2024. p. 219-223. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230959

Zhang, Jianqiu ; Drawz, Paul E. ; Simon, Gyorgy et al. / Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge. MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. editor / Jen Bichel-Findlay ; Paula Otero ; Philip Scott ; Elaine Huesing. IOS Press BV, 2024. pp. 219-223 (Studies in Health Technology and Informatics).

@inproceedings{c081bf112a08478182df93c6fbc925fa,

title = "Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge",

abstract = "Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.",

keywords = "Recurrent AKI, risk prediction",

author = "Jianqiu Zhang and Drawz, {Paul E.} and Gyorgy Simon and Adam, {Terrence J.} and Melton, {Genevieve B.}",

note = "Publisher Copyright: {\textcopyright} 2024 International Medical Informatics Association (IMIA) and IOS Press.; 19th World Congress on Medical and Health Informatics, MedInfo 2023 ; Conference date: 08-07-2023 Through 12-07-2023",

year = "2024",

month = jan,

day = "25",

doi = "10.3233/SHTI230959",

language = "English (US)",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "219--223",

editor = "Jen Bichel-Findlay and Paula Otero and Philip Scott and Elaine Huesing",

booktitle = "MEDINFO 2023 - The Future is Accessible",

address = "Netherlands",

}

TY - GEN

T1 - Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge

AU - Zhang, Jianqiu

AU - Drawz, Paul E.

AU - Simon, Gyorgy

AU - Adam, Terrence J.

AU - Melton, Genevieve B.

PY - 2024/1/25

Y1 - 2024/1/25

N2 - Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

AB - Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

KW - Recurrent AKI

KW - risk prediction

UR - http://www.scopus.com/inward/record.url?scp=85183586461&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85183586461&partnerID=8YFLogxK

U2 - 10.3233/SHTI230959

DO - 10.3233/SHTI230959

M3 - Conference contribution

C2 - 38269797

AN - SCOPUS:85183586461

T3 - Studies in Health Technology and Informatics

SP - 219

EP - 223

BT - MEDINFO 2023 - The Future is Accessible

A2 - Bichel-Findlay, Jen

A2 - Otero, Paula

A2 - Scott, Philip

A2 - Huesing, Elaine

PB - IOS Press BV

T2 - 19th World Congress on Medical and Health Informatics, MedInfo 2023

Y2 - 8 July 2023 through 12 July 2023

ER -

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge

Abstract

Publication series

Conference

Bibliographical note

Keywords

PubMed: MeSH publication types

Publisher link

Find It

Other files and links

Fingerprint

Cite this