Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Wagholikar, Kavishwar B.; Vijayraghavan, Sundararajan; Deshpande, Ashok

doi:10.1007/s10916-011-9780-4

Cited by 96 publications

(58 citation statements)

References 191 publications

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“…With the emergence of Medical Internet of Things (MIoT), real-time remote monitoring becomes feasible [36][37][38]. By collecting and combining MIoT information with hybrid imaging data, the accuracy of predictive analytics can be increased significantly [38] and can result in automated, Clinical Decision Support systems (CDSS) [39] (Figure 4). This leads to healthcare approaches that help improve patient comfort and reduce healthcare costs as part of a fully patient-centric, personalized medicine [40] (Figure 5).…”

Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

“…Machine Learning together with widely-accessible medical Big Data promises an era, where computer-aided diagnosis (CAD) and clinical decision support systems (CDSS) will contribute to routine decision making [39]. Artificial Intelligence (AI) assistants [249] will be able to process and provide personalized, real-time feedback to individuals over their Medical Big Data through their smartphones.…”

Section: Doctor In Pocketmentioning

confidence: 99%

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Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

Section: Doctor In Pocketmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…The main types of non-knowledge-based system use genetic algorith m, and artificial neural network [3]. Artificial neural network learns fro m experiment to simulate hu man thinking.…”

Section: B Non-knowledge-based Systemmentioning

confidence: 99%

The Implementation of Clinical Decision Support System: A Case Study in Saudi Arabia

Alqahtani¹,

Alshahri²,

Almaleh³

et al. 2016

IJITCS

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“…Their feasible implementation in Computer-Aided Diagnosis (CAD) methodologies has given new insights in the development of innovative and effective decision support systems for CVD premature risk assessment [15,[17][18][19]. An interesting approach is the exploration of different classifiers, as it was proposed by Jovic and Bogunovic [20].…”

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

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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

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Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Cited by 96 publications

References 191 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

The Implementation of Clinical Decision Support System: A Case Study in Saudi Arabia

Cardiovascular risk analysis by means of pulse morphology and clustering methodologies

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