scholarly journals Acute Leukemia Classification Using Convolution Neural Network in Clinical Decision Support System

2017 ◽  
Author(s):  
Thanh TTP ◽  
Giao N. Pham ◽  
Jin-Hyeok Park ◽  
Kwang-Seok Moon ◽  
Suk-Hwan Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Acute Leukemia ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Convolution Neural Network

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.

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

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.

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