Mining the Web and Literature to Discover New Knowledge about Diabetes

Marir, Farhi; Said, Huwida; Al-Obeidat, Feras

doi:10.1016/j.procs.2016.04.261

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…Studies were from nine countries including seven from USA [ 33 – 39 ], five from Canada [ 13 , 40 – 43 ], three from the United Kingdom [ 9 , 15 , 44 ], and one each from Australia [ 45 ], Dubai [ 46 ], Sweden [ 16 ], Italy [ 47 ], Germany [ 48 ] and Taiwan [ 49 ]. Nine studies were qualitative methods [ 15 , 16 , 34 , 36 – 38 , 41 , 43 , 49 ], four used mixed methods [ 35 , 39 , 42 , 48 ], three were media reviews [ 40 , 46 , 47 ], two narrative reviews [ 9 , 13 ], two descriptive studies [ 33 , 44 ] and one was a scoping review [ 45 ]. Three studies were published graduate dissertations [ 9 , 37 , 38 ], with one being a narrative review [ 9 ] and two using qualitative methods [ 37 , 38 ].…”

Section: Resultsmentioning

confidence: 99%

The use of digital storytelling of patients’ stories as an approach to translating knowledge: a scoping review

2021

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Background A growing interest has centered on digital storytelling in health research, described as a multi-media presentation of a story using technology. The use of digital storytelling in knowledge translation (KT) is emerging as technology advances in healthcare to address the challenging tasks of disseminating and transferring knowledge to key stakeholders. We conducted a scoping review of the literature available on the use of patient digital storytelling as a tool in KT interventions. Methods We followed by Arksey and O’Malley (Int J Soc Res Methodol 8(1):19–32, 2005), and Levac et al. (Implement Sci 5(1):69, 2010) recommended steps for scoping reviews. Search strategies were conducted for electronic databases (Medline, CINAHL, Web of Science, ProQuest dissertations and theses global, Clinicaltrials.gov and Psychinfo). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was used to report the review process. Results Of 4656 citations retrieved, 114 full texts were reviewed, and twenty-one articles included in the review. Included studies were from nine countries and focused on an array of physical and mental health conditions. A broad range of interpretations of digital storytelling and a variety of KT interventions were identified. Digital storytelling was predominately defined as a story in multi-media form, presented as a video, for selective or public viewing and used as educational material for healthcare professionals, patients and families. Conclusion Using digital storytelling as a tool in KT interventions can contribute to shared decision-making in healthcare and increase awareness in patients’ health related experiences. Concerns centered on the accuracy and reliability of some of the information available online and the impact of digital storytelling on knowledge action and implementation.

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Section: Resultsmentioning

confidence: 99%

The use of digital storytelling of patients’ stories as an approach to translating knowledge: a scoping review

2021

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“…It may locate clusters by constructing a density function that reflects the space distribution of the data points [9].…”

Section: Model Based Clustering Methodmentioning

confidence: 99%

“…It is a form of unsupervised learning that means how data should be group the data objects (similar types) together will be not known in advance. Clustering technique defines classes and put objects which are related to them in one class on the other hand in classification objects are placed in predefined classes [9]. The clustering algorithms can be generally classified into five types:…”

Section: Clustering Algorithmmentioning

confidence: 99%

Improved Efficiency of Nosql Using Optimized K-Mode

2017

IJAERD

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“…Herbert et al [6] concluded that HbA1c (glycated haemoglobin) is one of diabetes causing factor. Farhi et al [7] used data mining to retrieve novel knowledge which was not known before. Seokho et al [8] used svm machine learning algorithm for disease analysis.…”

Section: Related Workmentioning

confidence: 99%

Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

Alehegn¹,

Joshi²

2019

IJEAT

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In 21th century, IT plays a very important and helpful role in health care industries acting as a savior to human life. Data mining and machine learning are two sides of healthcare-IT. Proposed system considers one of the most common chronic diseases called diabetes. India and almost all other countries are worried about diabetic patients, so diabetes can termed as a global chronic disease. In this paper, well-known predictive machine learning techniques viz. SVM, Random Tree and ANN are applied on PIMA dataset. Results of SVM, ANN, and RT are 90.1%, 88.02%, and 83.59% respectively

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Mining the Web and Literature to Discover New Knowledge about Diabetes

Cited by 10 publications

References 8 publications

The use of digital storytelling of patients’ stories as an approach to translating knowledge: a scoping review

The use of digital storytelling of patients’ stories as an approach to translating knowledge: a scoping review

Improved Efficiency of Nosql Using Optimized K-Mode

Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

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