Mortality prediction system for heart failure with orthogonal relief and dynamic radius means.
Objective: This paper constructs a mortality prediction system based on a real-world dataset. This mortality prediction system aims to predict mortality in heart failure (HF) patients. Effective mortality prediction can improve resources allocation and clinical outcomes, avoiding inappropriate overt...
| Publicado en: | International Journal of Medical Informatics Vol. 115; pp. 10 - 18 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jul2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129683426&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129683426 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Jul2018 vid: 115 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 129683426 129683426 NLM29779711 129683426 10.1016/j.ijmedinf.2018.04.003 NLM29779711 129683426 ppf: 10 ppct: 8 formats: tig: atl: Mortality prediction system for heart failure with orthogonal relief and dynamic radius means. aug: au: Wang, Zhe Yao, Lijuan Li, Dongdong Ruan, Tong Liu, Min Gao, Ju affil: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China sug: subj: Heart Failure Mortality Models, Statistical Human Algorithms Hospital Mortality Aged Female Heart Failure Therapy China Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Scales Aged: 65+ years Female ab: Objective: This paper constructs a mortality prediction system based on a real-world dataset. This mortality prediction system aims to predict mortality in heart failure (HF) patients. Effective mortality prediction can improve resources allocation and clinical outcomes, avoiding inappropriate overtreatment of low-mortality patients and discharging of high-mortality patients. This system covers three mortality prediction targets: prediction of in-hospital mortality, prediction of 30-day mortality and prediction of 1-year mortality.Materials and Methods: HF data are collected from the Shanghai Shuguang hospital. 10,203 in-patients records are extracted from encounters occurring between March 2009 and April 2016. The records involve 4682 patients, including 539 death cases. A feature selection method called Orthogonal Relief (OR) algorithm is first used to reduce the dimensionality. Then, a classification algorithm named Dynamic Radius Means (DRM) is proposed to predict the mortality in HF patients.Results and Discussions: The comparative experimental results demonstrate that mortality prediction system achieves high performance in all targets by DRM. It is noteworthy that the performance of in-hospital mortality prediction achieves 87.3% in AUC (35.07% improvement). Moreover, the AUC of 30-day and 1-year mortality prediction reach to 88.45% and 84.84%, respectively. Especially, the system could keep itself effective and not deteriorate when the dimension of samples is sharply reduced.Conclusions: The proposed system with its own method DRM can predict mortality in HF patients and achieve high performance in all three mortality targets. Furthermore, effective feature selection strategy can boost the system. This system shows its importance in real-world applications, assisting clinicians in HF treatment by providing crucial decision information. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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