Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

Vukičević, Milan; Radovanović, Sandro; Milovanović, Miloš; Minović, Miroslav

doi:10.1155/2014/859279

Cited by 16 publications

(10 citation statements)

References 45 publications

(73 reference statements)

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“…Many architectures and frameworks have been introduced for cloud and IoT based medical monitoring systems. For instance, a cloud‐based approach was presented in the work of Vukićević et al that combined meta‐learning framework to rank and choose the best predictive methods for big data technologies to analyze biomedical data. Moreover, Bhatia and Sood offered an IoT‐based framework for patient tracking in ICU.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, machine learning methods such as decision tree, support vector machine (SVM), hidden Markov model, and Gaussian mixture model are discussed according to their efficiency and challenges in healthcare direction. A cloud‐based approach was presented in the work of Vukićević et al that combined meta‐learning framework to rank and choose the best predictive methods for big data technologies to analyze biomedical data.…”

Section: Related Workmentioning

confidence: 99%

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A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

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Advanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Other features of the ecosystem include indexing and search capabilities similar to DataMed [ 51 ] and a metalearning framework for ranking and selection of the best predictive algorithms [ 52 ]. Many of the bioinformatics software tools that we have discussed in the previous section have been successfully deployed in cloud environments and can be adapted to the commons ecosystem, including Apache Spark, a successor to Apache Hadoop and MapReduce for data analysis of Next Generation Sequencing Data [ 53 ].…”

Section: Developing a Cloud-based Digital Ecosystem For Biomedical Rementioning

confidence: 99%

Cloud computing applications for biomedical science: A perspective

Navale

Bourne

2018

PLoS Comput Biol

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Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

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“…One of the "soft" approaches for reducing the need for computer power in data analysis, is the introduction of meta-learning systems for selection and ranking of the best-suited algorithms for different datasets (21). This study aimed to evaluate and compare the prediction accuracy of APRI and FIB-4 versus data mining techniques (selecting the best performing algorithm), in the prediction of advanced fibrosis in patients with chronic HCV infection, using clinical information, serum biomarkers, in addition to IL28B rs12979860 SNP genotype as input data.…”

Section: Introductionmentioning

confidence: 99%