AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach

Issariya Uboltham; Nakornthip Prompoon; Wirichada Pan-Ngum

doi:10.1109/ICIS.2016.7550749

AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach

Issariya Uboltham
, Nakornthip Prompoon
, Wirichada Pan-Ngum

Chulalongkorn University
Mahidol University

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

2 Citations (Scopus)

Abstract

Acute Kidney Injury (AKI) is common and harmful disorder in hospitalized patients. It is associated with poor outcomes such as a decrease chance of survival, longer hospital stays and an increase progression of chronic kidney disease. To diagnosis AKI, the KDIGO clinical practice guideline has been published for providing standardized criteria of AKI definition and the recommendation in medical pathway. Moreover, early detection of AKI in patient at risk can also improve the outcomes. This paper presents an approach to assist the doctor in diagnosis and decision making process. First, the risk factors of AKI were identified using data mining approach based on Decision Tree classification technique. Simple Cart and J48 were selected as the algorithms for this process. Second, a concept of tool requirements and design named 'AKIHelper' is presented. This tool is created based on KDIGO guideline which is expected to use for diagnosis and staging severity of AKI.

Original language	English
Title of host publication	2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
Editors	Kuniaki Uehara, Masahide Nakamura
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509008063
DOIs	https://doi.org/10.1109/ICIS.2016.7550749
Publication status	Published - 23 Aug 2016
Externally published	Yes
Event	15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan Duration: 26 Jun 2016 → 29 Jun 2016

Publication series

Name	2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

Conference

Conference	15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
Country/Territory	Japan
City	Okayama
Period	26/06/16 → 29/06/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

AKI
Acute Kidney Injury
Classification
Data Mining
Decision Tree
Diagnostic Tool
E-Health
Healthcare
KDIGO
Risk Factors

Access to Document

10.1109/ICIS.2016.7550749

Cite this

Uboltham, I., Prompoon, N., & Pan-Ngum, W. (2016). AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach. In K. Uehara, & M. Nakamura (Eds.), 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings Article 7550749 (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIS.2016.7550749

Uboltham, Issariya ; Prompoon, Nakornthip ; Pan-Ngum, Wirichada. / AKIHelper : Acute kidney injury diagnostic tool using KDIGO guideline approach. 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings. editor / Kuniaki Uehara ; Masahide Nakamura. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings).

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title = "AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach",

abstract = "Acute Kidney Injury (AKI) is common and harmful disorder in hospitalized patients. It is associated with poor outcomes such as a decrease chance of survival, longer hospital stays and an increase progression of chronic kidney disease. To diagnosis AKI, the KDIGO clinical practice guideline has been published for providing standardized criteria of AKI definition and the recommendation in medical pathway. Moreover, early detection of AKI in patient at risk can also improve the outcomes. This paper presents an approach to assist the doctor in diagnosis and decision making process. First, the risk factors of AKI were identified using data mining approach based on Decision Tree classification technique. Simple Cart and J48 were selected as the algorithms for this process. Second, a concept of tool requirements and design named 'AKIHelper' is presented. This tool is created based on KDIGO guideline which is expected to use for diagnosis and staging severity of AKI.",

keywords = "AKI, Acute Kidney Injury, Classification, Data Mining, Decision Tree, Diagnostic Tool, E-Health, Healthcare, KDIGO, Risk Factors",

author = "Issariya Uboltham and Nakornthip Prompoon and Wirichada Pan-Ngum",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 ; Conference date: 26-06-2016 Through 29-06-2016",

year = "2016",

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doi = "10.1109/ICIS.2016.7550749",

language = "English",

series = "2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings",

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Uboltham, I, Prompoon, N & Pan-Ngum, W 2016, AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach. in K Uehara & M Nakamura (eds), 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings., 7550749, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016, Okayama, Japan, 26/06/16. https://doi.org/10.1109/ICIS.2016.7550749

AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach. / Uboltham, Issariya; Prompoon, Nakornthip; Pan-Ngum, Wirichada.
2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings. ed. / Kuniaki Uehara; Masahide Nakamura. Institute of Electrical and Electronics Engineers Inc., 2016. 7550749 (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings).

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

TY - GEN

T1 - AKIHelper

T2 - 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016

AU - Uboltham, Issariya

AU - Prompoon, Nakornthip

AU - Pan-Ngum, Wirichada

PY - 2016/8/23

Y1 - 2016/8/23

N2 - Acute Kidney Injury (AKI) is common and harmful disorder in hospitalized patients. It is associated with poor outcomes such as a decrease chance of survival, longer hospital stays and an increase progression of chronic kidney disease. To diagnosis AKI, the KDIGO clinical practice guideline has been published for providing standardized criteria of AKI definition and the recommendation in medical pathway. Moreover, early detection of AKI in patient at risk can also improve the outcomes. This paper presents an approach to assist the doctor in diagnosis and decision making process. First, the risk factors of AKI were identified using data mining approach based on Decision Tree classification technique. Simple Cart and J48 were selected as the algorithms for this process. Second, a concept of tool requirements and design named 'AKIHelper' is presented. This tool is created based on KDIGO guideline which is expected to use for diagnosis and staging severity of AKI.

AB - Acute Kidney Injury (AKI) is common and harmful disorder in hospitalized patients. It is associated with poor outcomes such as a decrease chance of survival, longer hospital stays and an increase progression of chronic kidney disease. To diagnosis AKI, the KDIGO clinical practice guideline has been published for providing standardized criteria of AKI definition and the recommendation in medical pathway. Moreover, early detection of AKI in patient at risk can also improve the outcomes. This paper presents an approach to assist the doctor in diagnosis and decision making process. First, the risk factors of AKI were identified using data mining approach based on Decision Tree classification technique. Simple Cart and J48 were selected as the algorithms for this process. Second, a concept of tool requirements and design named 'AKIHelper' is presented. This tool is created based on KDIGO guideline which is expected to use for diagnosis and staging severity of AKI.

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KW - Acute Kidney Injury

KW - Classification

KW - Data Mining

KW - Decision Tree

KW - Diagnostic Tool

KW - E-Health

KW - Healthcare

KW - KDIGO

KW - Risk Factors

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M3 - Conference contribution

AN - SCOPUS:84988019713

T3 - 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

BT - 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

A2 - Uehara, Kuniaki

A2 - Nakamura, Masahide

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 26 June 2016 through 29 June 2016

ER -

Uboltham I, Prompoon N, Pan-Ngum W. AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach. In Uehara K, Nakamura M, editors, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7550749. (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings). doi: 10.1109/ICIS.2016.7550749

AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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