AALIM: Multimodal mining for cardiac decision support

Syeda-Mahmood, Tanveer; Wang, F.; Beymer, David; Amir, Arnon; Richmond, M.; Hashmi, S.

doi:10.1109/cic.2007.4745458

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…ith integrated information becoming available through large patient repositories, newer decision support systems are emerging that enable physicians to benefit from consensus opinions of other physicians who have looked at similar patients [1]. These are based on techniques of content-based retrieval where they use underlying similarity in patients' diagnostic data to infer the similarity in their diagnosed diseases [1].…”

Section: Introductionmentioning

confidence: 99%

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Similarity retrieval of cardiac reports

Syeda-Mahmood

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Mining medical reports can reveal important information correlating diagnosis with raw measurements helping in decision support. In this paper we address the problem of finding similar measurement reports for aiding clinical decision support. Specifically, we present a new approach to generating document class models for measurement reports using a multi-scale feature-value kernel. The class models serve as natural feature selection mechanism as well as indexes to large report collections. A document retrieval algorithm based on document class models is presented to enable similarity retrieval of pre-diagnosed reports. Collaborative filtering-guided assembly of associated disease labels is used to achieve clinical decision support.

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Section: Introductionmentioning

confidence: 99%

“…These are based on techniques of content-based retrieval where they use underlying similarity in patients' diagnostic data to infer the similarity in their diagnosed diseases [1]. An important source of diagnostic information are the measurement reports.…”

Section: Introductionmentioning

confidence: 99%

Similarity retrieval of cardiac reports

Syeda-Mahmood

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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“…The rapid implementation and permanent updating of clinical decision support methods and algorithms could be easily done in central server. Examples of such systems for remote ECG data analysis and automated retinal image analysis using Internet are reported in [1], [2] and [3] respectively. The huge computational resources using parallel computing methods in emerging Grid technology become available in the network based clinical decision support system with distributed resources.…”

Section: Introduction I Mplementation Of Applications Of Informationmentioning

confidence: 99%

Network based clinical decision support system

Jegelevičius

Kriščiukaitis

Lukoševičius

et al. 2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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Developed prototype of network based clinical decision support system consists of database of clinical data and web-based applications for signal and image analysis methods and algorithms. The methods for eye fundus image analysis and ECG P-wave morphology evaluation are the first methods covering two clinical specialties cardiology and ophthalmology in the system. Network based database and combined analysis of the parameters obtained by means of implemented methods gives a possibility of holistic approach in clinical decision support.

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“…Here supervised leaning we already know output values y based on below function way, used to find the w value with below equation (3): w=h.y^(-1) (3) Here in this paper of research, used weight value used matrix inversion showed above equation (3). Above two methods are discussed following next section, and its proved RBF NN accuracy is more compare than SVM -RBF [18].…”

Section: Liner Andmentioning

confidence: 99%

Digital Image Processing Assessment in Multi Slice CT Angiogram using Liner, Non-Liner and Both Classifiers

Krishna¹,

Shekar²

2017

IJERA

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Nowadays with heart diseases most of peoples are dying lock process to find problem agile fashion in remote areas. In this research help to peoples who are staying remote also to find problem in heart on which location and how much problem to near and finally gives best analysis methodology for automated analysis for MultiSlice CT Angiogram images. Multi slice CT scanner is used to identify heart disease. Multi-detector CT is considered convenient and reliable non-invasive imaging modality for assessment of human angiogram 3D images. Automatic hart segmentation from Computed Tomography (CT) is highly demanded. Accurate hart segmentation is a crucial for computer-aided heart disease diagnosis and treatment planning. After segmentation and future extraction then identify whether the patient angiogram waveform has disease or not. For that, Support Vector Machine method is used to confirm the presence of disease. Neural Networks can solve different types of nonlinear problems in image classification and retrieval process. After that majorly focus on research learning methodologies for improve performance of the Multi Slice CT Angiogram images. So, in this research is summarizing the problem with liner of RBF neural network, Non-Liner SVM and RBF NN with liner and non-liner. Final, RBF NN with liner and non-liner provided more value and proved as best compare with other existing methodologies. This methodology consumes less time for both learning as well as testing comparatively than any other methods like back propagation. This issue drastically improves the estimation efficiency and accuracy for real time 128, 256 slices CT scan angiogram images.

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AALIM: Multimodal mining for cardiac decision support

Cited by 19 publications

References 3 publications

Similarity retrieval of cardiac reports

Similarity retrieval of cardiac reports

Network based clinical decision support system

Digital Image Processing Assessment in Multi Slice CT Angiogram using Liner, Non-Liner and Both Classifiers

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