An Assessment of Patient Behavior Over Time–Periods: A Case Study of Managing Type 2 Diabetes Through Blood Glucose Readings and Insulin Doses

Namayanja, Josephine M.; Janeja, Vandana P.

doi:10.1007/s10916-012-9894-3

Cited by 11 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By the same token, Karahoca and Tunga [117] used High Dimensional Model Representation (HDMR) to manage the drug dosage planning process in T2D. Moreover, taking into consideration patient behavior in relation to patient care, Namayanja and Janeja used clustering techniques to improve insulin treatment in T2D patients [118].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,024

530

View full text Add to dashboard Cite

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

show abstract

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,024

530

View full text Add to dashboard Cite

show abstract

“…Namayanja et al . [ 18 ] exploited data mining and clustering to study the measurements in blood glucose and doses of regular INS for a selected number of patients.…”

Section: Related Workmentioning

confidence: 99%

Specification and Verification of Medical Monitoring System Using Petri-nets

Majma

Babamir

2014

J Med Signals Sens

View full text Add to dashboard Cite

To monitor the patient behavior, data are collected from patient's body by a medical monitoring device so as to calculate the output using embedded software. Incorrect calculations may endanger the patient's life if the software fails to meet the patient's requirements. Accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from the patient's body are fuzzy. Some methods have already dealt with monitoring the medical monitoring devices; however, model based monitoring fuzzy computations of such devices have been addressed less. The present paper aims to present synthesizing a fuzzy Petri-net (FPN) model to verify behavior of a sample medical monitoring device called continuous infusion insulin (INS) because Petri-net (PN) is one of the formal and visual methods to verify the software's behavior. The device is worn by the diabetic patients and then the software calculates the INS dose and makes a decision for injection. The input and output of the infusion INS software are not crisp in the real world; therefore, we present them in fuzzy variables. Afterwards, we use FPN instead of clear PN to model the fuzzy variables. The paper follows three steps to synthesize an FPN to deal with verification of the infusion INS device: (1) Definition of fuzzy variables, (2) definition of fuzzy rules and (3) design of the FPN model to verify the software behavior.

show abstract

“…Other studies have also referred the role of clustering methodologies for CVD assessment, such as the work developed by Haseena et al [26], where a fuzzy C-mean clustered probabilistic neural network for ECG beats discrimination was described. Clustering methodologies were also successful applied in other medical fields, such as in the identification of patterns in blood glucose measurements and regular insulin doses taken before meal time [27].…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular risk analysis by means of pulse morphology and clustering methodologies

Almeida

Borba

Pereira

et al. 2014

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 7 ( 2 0 1 4 ) [257][258][259][260][261][262][263][264][265][266] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b a b s t r a c tThe purpose of this study was the development of a clustering methodology to deal with arterial pressure waveform (APW) parameters to be used in the cardiovascular risk assessment. One hundred sixteen subjects were monitored and divided into two groups. The first one (23 hypertensive subjects) was analyzed using APW and biochemical parameters, while the remaining 93 healthy subjects were only evaluated through APW parameters. The expectation maximization (EM) and k-means algorithms were used in the cluster analysis, and the risk scores (the Framingham Risk Score (FRS), the Systematic COronary Risk Evaluation (SCORE) project, the Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) and the PROspective Cardiovascular Münster (PROCAM)), commonly used in clinical practice were selected to the cluster risk validation. The result from the clustering risk analysis showed a very significant correlation with ASSIGN (r = 0.582, p < 0.01) and a significant correlation with FRS (r = 0.458, p < 0.05). The results from the comparison of both groups also allowed to identify the cluster with higher cardiovascular risk in the healthy group. These results give new insights to explore this methodology in future scoring trials.© 2014 Elsevier Ireland Ltd. All rights reserved. IntroductionThe atherosclerotic cardiovascular disease (CVD) is the most common cause of death worldwide, resulting from the combination of several risk factors [1]. The international guidelines [2,3] consider that individuals with established CVD should be the first priority for preventive measures application. The concern in changing the current healthcare paradigm, from reactive towards preventive care, aims at identify individuals for risk in early stages of disease development, and then, * Corresponding author. Tel.: +351 239410109.E-mail address: vaniagalmeida@lei.fis.uc.pt (V.G. Almeida).direct more efforts and attention to the risk factors modification [4,5]. Fortunately, this is an emergent tendency that can be addressed using the traditional risk scores, but also using innovative predictive algorithms. During the last years many risk estimation systems have been developed in order to assist clinicians in the risk assessment, and in the individual chances prediction, for CVD development. The major challenges of these tools are the capabilities to: (1) identify high risk individuals, (2) weight the individual effects of all risk factors, (3) stratify or organize who needs lifestyle advice or medical therapy, and finally (4) avoid http://dx

show abstract

An Assessment of Patient Behavior Over Time–Periods: A Case Study of Managing Type 2 Diabetes Through Blood Glucose Readings and Insulin Doses

Cited by 11 publications

References 13 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

Specification and Verification of Medical Monitoring System Using Petri-nets

Cardiovascular risk analysis by means of pulse morphology and clustering methodologies

Contact Info

Product

Resources

About