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

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 - https://www.scopus.com/pages/publications/85183586461

UR - https://www.scopus.com/pages/publications/85183586461#tab=citedBy

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

Publisher link

Find It

Other files and links

Fingerprint

Cite this