Impact of data quality assessment on development of clinical predictive models.
Data quality plays a very important role in predicting clinical outcomes. Data quality is multi dimensional and most relevant studies consider just one or two dimensions. In this study a systematic data quality assessment is performed using four data dimensions. The results demonstrate that performance of predictive models improves when the quality of the data is assessed and addressed systematically.
School of Public Health and Community Medicine, UNSW Australia.
Jitendra
J
Jonnagaddala
School of Public Health and Community Medicine, UNSW Australia.
Siaw-Teng
ST
Liaw
Asia-Pacific Ubiquitous Healthcare Research Centre, UNSW Australia.
Pradeep
P
Ray
D003198:Computer Simulation; D003422:Critical Care / Q000706:statistics & numerical data; D016638:Critical Illness / Q000401:mortality*; D000068598:Data Accuracy; D066264:Datasets as Topic / Q000592:standards; D020000:Decision Support Systems, Clinical / Q000592:standards*; D057286:Electronic Health Records / Q000592:standards* / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D015994:Incidence; D015233:Models, Statistical; D009517:New South Wales; D011379:Prognosis; D011785:Quality Assurance, Health Care / Q000379:methods*; D018570:Risk Assessment / Q000592:standards; D016019:Survival Analysis