scholarly journals Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Rio Kurniawan ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Learning Process ◽  
Clinical Decision Support System ◽  
Early Stage ◽  
Clinical Decision

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is  part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

Clinical Decision Support System for Early Disease Detection and Management

2019 ◽  
pp. 108-155
Author(s):  
Likewin Thomas ◽  
Manoj Kumar M. V. ◽  
Annappa B.
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medical Error ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

Clinical Decision Support System for Early Disease Detection and Management

2021 ◽  
pp. 1035-1075
Author(s):  
Likewin Thomas ◽  
Manoj Kumar M. V. ◽  
Annappa B.
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medical Error ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

scholarly journals A Clinical Decision-Support System Based on Three-Stage Integrated Image Analysis for Diagnosing Lung Disease

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 386
Author(s):  
Ching-Hsue Cheng ◽  
Hsien-Hsiu Chen ◽  
Tai-Liang Chen
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Decision Support ◽  
Decision Support System ◽  
Lung Disease ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Ct Image

Thoracic computed tomography (CT) technology has been used for lung cancer screening in high-risk populations, and this technique is highly effective in the identification of early lung cancer. With the rapid development of intelligent image analysis in the field of medical science and technology, many researchers have proposed computer-aided automatic diagnosis methods for facilitating medical experts in detecting lung nodules. This paper proposes an advanced clinical decision-support system for analyzing chest CT images of lung disease. Three advanced methods are utilized in the proposed system: the three-stage automated segmentation method (TSASM), the discrete wavelet packets transform (DWPT) with singular value decomposition (SVD), and the algorithms of the rough set theory, which comprise a classification-based method. Two collected medical CT image datasets were prepared to evaluate the proposed system. The CT image datasets were labeled (nodule, non-nodule, or inflammation) by experienced radiologists from a regional teaching hospital. According to the results, the proposed system outperforms other classification methods (trees, naïve Bayes, multilayer perception, and sequential minimal optimization) in terms of classification accuracy and can be employed as a clinical decision-support system for diagnosing lung disease.

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