Prediction of Length of ICU Stay Using Data-mining Techniques: an Example of Old Critically Ill Postoperative Gastric Cancer Patients | Zendy

Xiaochun Zhang | Zendy; Zhidan Zhang | Zendy; Desheng Huang | Zendy

Open Access

Prediction of Length of ICU Stay Using Data-mining Techniques: an Example of Old Critically Ill Postoperative Gastric Cancer Patients

Author(s) -

Xiaochun Zhang,

Zhidan Zhang,

Desheng Huang

Publication year - 2012

Publication title -

asian pacific journal of cancer prevention

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.512

H-Index - 75

eISSN - 2476-762X

pISSN - 1513-7368

DOI - 10.7314/apjcp.2012.13.1.097

Subject(s) - medicine , intensive care unit , comorbidity , sofa score , emergency medicine , intensive care medicine , multivariate analysis , univariate , saps ii , univariate analysis , population , intensive care , multivariate statistics , proportional hazards model , apache ii , statistics , mathematics , environmental health

With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction.

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