Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Fallah, Mina; Kalhori, Sharareh R. Niakan

doi:10.4258/hir.2017.23.4.262

Cited by 19 publications

(15 citation statements)

References 34 publications

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“…In our colonoscopy video, a 0.5 s period yielded optimal results. As the standard setting, observing the colonoscopy video at a speed of 0.7 times to avoid misreading directions, participation of five fellow doctors, and selecting values more than three made the accuracy of the gold standard more reliable (Cho et al, 2016; Fallah & Niakan Kalhori, 2017; Cho et al, 2017)…”

Section: Discussionmentioning

confidence: 99%

“…The up, down, and space buttons were matched to insert, withdrawal, and stop, respectively, and the frames were classified based on the input keyboard values. Five gastroenterologist with more than 5 years experience participated in this work, and the values selected by more than three were used as the standard (Fallah & Niakan Kalhori, 2017). An overview of the entire process is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Cho

Kim

Hong

et al. 2019

PeerJ

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Background Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum. Methods In order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest. Results A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer. Conclusion Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Cho

Kim

Hong

et al. 2019

PeerJ

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“…The 11 attributes included 10 as input attributes and the one as the target attribute. The target attribute consisted of two classes: one class obtained the diagnosis of diabetes tested positive and the second class was tested negative by the Kmean finding within the clusters that are more related to each other at the significance level of 0.05 [35].…”

Section: Data Mining Platformmentioning

confidence: 99%

An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria

et al. 2019

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The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes.

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“…AI algorithms have the potential to deliver better care especially in combination with recent technologies [24]. Because modern medicine has faced challenges regarding a large amount of data acquisition, analysis, and the application of the obtained knowledge to solving complex clinical problems, it is necessary to use AI capabilities for these purposes [192125]. AI is composed of various intelligent algorithms and techniques, such as machine learning (ML), natural language processing (NLP), robotics, fuzzy logic (FL), expert systems (ES), knowledge base (KB), and the mix of two or more methods (multimethods) [2123].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

et al. 2019

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ObjectivesThe incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care.MethodsThis is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives.ResultsThe main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables.ConclusionsIt is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

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Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Cited by 19 publications

References 34 publications

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

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