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Data Mining for Improving Health-Car...

He, Nannan.

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Data Mining for Improving Health-Care Resource Deployment.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Mining for Improving Health-Care Resource Deployment./
作者:	He, Nannan.
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1554229
ISBN:	9781303842337

Data Mining for Improving Health-Care Resource Deployment.
He, Nannan.

Data Mining for Improving Health-Care Resource Deployment. - 59 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.S.)--University of California, Santa Cruz, 2014.

Data Mining for Improving Health-Care Resource Deployment Nannan He While the health care industry accounts for a significant large portion of the GDP, the health care system in the US are still relatively inefficient. Before cutting down unnecessary health care expenses, it is important to ensure that individuals who really need medical attention should receive it. For example, if we could predict the hospitalization period (in days) for a potential patient, then we could better predict and distribute health care resources.

ISBN: 9781303842337Subjects--Topical Terms:

1030799
Information Technology.

Data Mining for Improving Health-Care Resource Deployment.
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520 $a Data Mining for Improving Health-Care Resource Deployment Nannan He While the health care industry accounts for a significant large portion of the GDP, the health care system in the US are still relatively inefficient. Before cutting down unnecessary health care expenses, it is important to ensure that individuals who really need medical attention should receive it. For example, if we could predict the hospitalization period (in days) for a potential patient, then we could better predict and distribute health care resources.
520 $a In this research, we apply data mining methods and tools to address the problem of predicting future hospitalization periods (in days) for patients from a given set of historical patient data. The data mining techniques that we explored were linear regression, random forest and gradient boosting. For each technique, we used different historical data sets. The combination of data mining techniques and historical datasets enabled us to compare access and choose the combination which provides the best prediction of hospitalization period of a set of patients. Based on the results of our work, the random forest technique provides the best prediction of patient hospitalization.
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