Abstract：
BACKGROUND:Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. METHODS:In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k-Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. RESULTS:Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. CONCLUSION:Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.
journal_name
BMC Med Inform Decis Makjournal_title
BMC medical informatics and decision makingauthors
Alloghani M,Aljaaf A,Hussain A,Baker T,Mustafina J,Al-Jumeily D,Khalaf Mdoi
10.1186/s12911-019-0990-xsubject
Has Abstractpub_date
2019-12-12 00:00:00pages
253issue
Suppl 9issn
1472-6947pii
10.1186/s12911-019-0990-xjournal_volume
19pub_type
杂志文章abstract：BACKGROUND:Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measure...
journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-020-01345-1
更新日期：2021-01-06 00:00:00
abstract：BACKGROUND:Methods for linking real-world healthcare data often use a latent class model, where the latent, or unknown, class is the true match status of candidate record-pairs. This commonly used model assumes that agreement patterns among multiple fields within a latent class are independent. When this assumption is ...
journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/1472-6947-13-97
更新日期：2013-08-30 00:00:00
abstract：BACKGROUND:Demographic change with its consequences of an aging society and an increase in the demand for care in the home environment has triggered intensive research activities in sensor devices and smart home technologies. While many advanced technologies are already available, there is still a lack of decision supp...
journal_title：BMC medical informatics and decision making
pub_type： 杂志文章,评审
doi：10.1186/1472-6947-12-43
更新日期：2012-05-28 00:00:00
abstract：BACKGROUND:In recent years, inter-municipal cooperation in healthcare services has been an important measure implemented to meet future demographic changes in western countries. This entails an increased focus on communication and information sharing across organisational borders. Technology enables efficient and effec...
journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-018-0701-z
更新日期：2018-11-07 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-018-0654-2
更新日期：2018-09-14 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-019-0998-2
更新日期：2019-12-10 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 临床试验,杂志文章
doi：10.1186/1472-6947-13-29
更新日期：2013-02-28 00:00:00
abstract：BACKGROUND:New Specific Application Domain (SAD) heuristics or design principles are being developed to guide the design and evaluation of mobile applications in a bid to improve on the usability of these applications. This is because the existing heuristics are rather generic and are often unable to reveal a large num...
journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-018-0718-3
更新日期：2019-01-09 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 临床试验,杂志文章
doi：10.1186/s12911-016-0387-z
更新日期：2016-11-24 00:00:00
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journal_title：BMC medical informatics and decision making
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/1472-6947-13-134
更新日期：2013-12-09 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章,评审
doi：10.1186/s12911-016-0359-3
更新日期：2016-09-15 00:00:00
abstract：BACKGROUND:Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia. METHODS:Sensitivity and sp...
journal_title：BMC medical informatics and decision making
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更新日期：2019-12-30 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章,多中心研究
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更新日期：2012-05-23 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/1472-6947-14-98
更新日期：2014-12-23 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 评论,社论
doi：10.1186/1472-6947-9-20
更新日期：2009-04-02 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
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更新日期：2019-08-24 00:00:00
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/s12911-016-0294-3
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abstract：:After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617. ...
journal_title：BMC medical informatics and decision making
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pub_type： 杂志文章,随机对照试验
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pub_type： 杂志文章,评审
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更新日期：2004-06-28 00:00:00
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journal_title：BMC medical informatics and decision making
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更新日期：2020-07-09 00:00:00
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journal_title：BMC medical informatics and decision making
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doi：10.1186/s12911-016-0245-z
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
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pub_type： 杂志文章
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pub_type： 杂志文章
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pub_type： 杂志文章
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