Network based clinical decision support system

Jegelevičius, Darius; Kriščiukaitis, Algimantas; Lukoševičius, Arūnas; Marozas, Vaidotas; Paunksnis, Alvydas; Barzdžiukas, Valerijus; Patašius, Martynas; Buteikienė, Dovilė; Vainoras, Alfonsas; Gargasas, Liudas

doi:10.1109/itab.2009.5394348

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…In the standard case, the distance used, is the Euclidean distance. In the RBF kernel, the parameters determine, the width of the kernel, and d(x, y) is the distance metric [15].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A Survey on Various Classification Techniques for Medical Image Data

Chatap¹,

Shrivastava²

2014

IJCA

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In this paper a survey on various classification techniques for medical image and also its application for detection of many diseases. Several classification techniques are investigated till today. One of the best methods for classification techniques artificial neural network and SVM (Support Vector Machine). In past many classification techniques by using GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) are commonly used. The classification techniques provide invaluable information to pathologist for diagnosis and treatment of diseases. By identifying and counting blood cell within the blood smear using classification techniques it's quite possible to detect so many diseases. If we use one of the new classifier i.e. nearest neighbor and SVM it is quiet possible to detect the cancer cell from the blood cell counting.

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“…In the standard case, the distance used, is the Euclidean distance. In the RBF kernel, the parameters determine, the width of the kernel, and d(x, y) is the distance metric [15].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A Survey on Various Classification Techniques for Medical Image Data

Chatap¹,

Shrivastava²

2014

IJCA

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“…The authors of [13] proposed a practical guideline based clinical decision support system for metabolism synthesis. First they established the medical logic model and then converted it in to the rule set for inference.…”

Section: Related Workmentioning

confidence: 99%

Socially interactive CDSS for u-life care

Fatima

Fahim

Guan

et al. 2011

Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication

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Clinical decision support system (CDSS) is an interactive decision support system computer software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data, disease prevention, and alerting adverse drug events. It links health observations with health knowledge to influence health choices by clinicians for improved health care. Different from conventional CDSSs which focus on diagnosis assistance, the focus of our CDSS is to provide recommendations and healthcare services for chronic disease patients by long term monitoring. To make our CDSS more intelligent, it induces the patients to interact with the system. By continuously learning and digesting patients' experience and knowledge, the knowledge base of our CDSS is self-evolutionary and dynamically enhanced. We mainly develop two modules to achieve the function of social interaction. Firstly, Knowledge Authority Module (KAM) is developed which is capable of manipulating and preprocessing social data. Secondly, to support self-evolutionary and dynamical learning, we designed the rough set based inference engine. Through social interaction, the patients can get continuous relevant medical recommendations from the system, so they can get a chance to improve their health conditions which in turns keeping on their quality of life.

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