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Blood glucose prediction models for ...
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Fernando, Warnakulasuriya Chandima Thilina.
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Blood glucose prediction models for personalized diabetes management.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Blood glucose prediction models for personalized diabetes management./
作者:
Fernando, Warnakulasuriya Chandima Thilina.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:
41 p.
附註:
Source: Masters Abstracts International, Volume: 56-01.
Contained By:
Masters Abstracts International56-01(E).
標題:
Computer science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10149646
ISBN:
9781369047431
Blood glucose prediction models for personalized diabetes management.
Fernando, Warnakulasuriya Chandima Thilina.
Blood glucose prediction models for personalized diabetes management.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 41 p.
Source: Masters Abstracts International, Volume: 56-01.
Thesis (M.S.)--North Dakota State University, 2016.
Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.
ISBN: 9781369047431Subjects--Topical Terms:
523869
Computer science.
Blood glucose prediction models for personalized diabetes management.
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Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.
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