Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression.
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| Title: | Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression. |
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| Authors: | Cheng, Li-Chen1, Hu, Ya-Han2 yahan.hu@mis.ccu.edu.tw, Chiou, Shr-Han2 |
| Source: | Journal of Medical Systems. May2017, Vol. 41 Issue 5, p1-12. 12p. |
| Subjects: | Chronic kidney failure, Statistics, Time series analysis, Data mining, Decision making in clinical medicine, Data analysis, Predictive tests, Receiver operating characteristic curves, Disease progression, Data analysis software, Descriptive statistics |
| Abstract: | Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m (end-stage renal disease, ESRD) 6 months after collecting their final laboratory test information by evaluating time-related features. A total of 463 CKD patients collected from January 2004 to December 2013 at one of the biggest dialysis centers in southern Taiwan were included in the experimental evaluation. We integrated the temporal abstraction (TA) technique with data mining methods to develop CKD progression prediction models. Specifically, the TA technique was used to extract vital features (TA-related features) from high-dimensional time-series data, after which several data mining techniques, including C4.5, classification and regression tree (CART), support vector machine, and adaptive boosting (AdaBoost), were applied to develop CKD progression prediction models. The results revealed that incorporating temporal information into the prediction models increased the efficiency of the models. The AdaBoost+CART model exhibited the most accurate prediction among the constructed models (Accuracy: 0.662, Sensitivity: 0.620, Specificity: 0.704, and AUC: 0.715). A number of TA-related features were found to be associated with the deterioration of renal function. These features can provide further clinical information to explain the progression of CKD. TA-related features extracted by long-term tracking of changes in laboratory test values can enable early diagnosis of ESRD. The developed models using these features can facilitate medical personnel in making clinical decisions to provide appropriate diagnoses and improved care quality to patients with CKD. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 122782823 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Li-Chen%22">Cheng, Li-Chen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hu%2C+Ya-Han%22">Hu, Ya-Han</searchLink><relatesTo>2</relatesTo><i> yahan.hu@mis.ccu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chiou%2C+Shr-Han%22">Chiou, Shr-Han</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Medical+Systems%22">Journal of Medical Systems</searchLink>. May2017, Vol. 41 Issue 5, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Chronic+kidney+failure%22">Chronic kidney failure</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making+in+clinical+medicine%22">Decision making in clinical medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+tests%22">Predictive tests</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+progression%22">Disease progression</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m (end-stage renal disease, ESRD) 6 months after collecting their final laboratory test information by evaluating time-related features. A total of 463 CKD patients collected from January 2004 to December 2013 at one of the biggest dialysis centers in southern Taiwan were included in the experimental evaluation. We integrated the temporal abstraction (TA) technique with data mining methods to develop CKD progression prediction models. Specifically, the TA technique was used to extract vital features (TA-related features) from high-dimensional time-series data, after which several data mining techniques, including C4.5, classification and regression tree (CART), support vector machine, and adaptive boosting (AdaBoost), were applied to develop CKD progression prediction models. The results revealed that incorporating temporal information into the prediction models increased the efficiency of the models. The AdaBoost+CART model exhibited the most accurate prediction among the constructed models (Accuracy: 0.662, Sensitivity: 0.620, Specificity: 0.704, and AUC: 0.715). A number of TA-related features were found to be associated with the deterioration of renal function. These features can provide further clinical information to explain the progression of CKD. TA-related features extracted by long-term tracking of changes in laboratory test values can enable early diagnosis of ESRD. The developed models using these features can facilitate medical personnel in making clinical decisions to provide appropriate diagnoses and improved care quality to patients with CKD. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10916-017-0732-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Chronic kidney failure Type: general – SubjectFull: Statistics Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Data mining Type: general – SubjectFull: Decision making in clinical medicine Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Predictive tests Type: general – SubjectFull: Receiver operating characteristic curves Type: general – SubjectFull: Disease progression Type: general – SubjectFull: Data analysis software Type: general – SubjectFull: Descriptive statistics Type: general Titles: – TitleFull: Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheng, Li-Chen – PersonEntity: Name: NameFull: Hu, Ya-Han – PersonEntity: Name: NameFull: Chiou, Shr-Han IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 01485598 Numbering: – Type: volume Value: 41 – Type: issue Value: 5 Titles: – TitleFull: Journal of Medical Systems Type: main |
| ResultId | 1 |
