Develop a Computer based basic Clinical Information System

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Develop a Computer based basic Clinical Information System

Unread postby UCERD.COM » Mon Jun 03, 2013 4:09 am

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Clinical Problems
Medical diagnosis is a complex and judgmental process, based not only on medical information derived from books and literatures and data obtained from various pathological tests, but also depends largely on experience, judgment and reasoning which essentially are the functions of human brain [1]. However, analytic decisions made by physicians are ¬random and highly variable (within one physician and between physicians) and often lacking explanation or rationalization [2,3]. However, in underdeveloped countries, doctors are barely available in rural areas. A recent statistical data shows that 75% of qualified consulting doctors reside in urban areas and another 23% in semi-urban areas, and only about 2% of doctors reside in rural areas, where, unfortunately, nearly 78% of Indians reside [4]. This has created an unwarranted imbalance in patient-doctor ratio of more than 10,000 patients for one doctor in rural India. Apart from the acute scarcity of physicians, lack of availability of electricity is quite common in rural areas of third world countries like India. Such prosaic problems necessitate the use of an inexpensive, portable, low power battery operated high-speed the doctors for necessary care.

Telecommunication and Internet
Over the past few decades, telecommunication has been used to transmit health related data of patients to remote locations [5–9]. But everywhere signals were transmitted for ultimate decision taking by human brains. Due to the large number of patients at the nodal center which are being diagnosed by the physician, it is not possible for the physician to efficiently monitor and diagnose each patient along the time. The medical resource becomes critical. An unnecessary imbalance in patient–doctor ratio has necessitated the development of equipment which can predict an imminent health hazard and can red alert the patients to contact doctor for necessary care. Advantage of patient profiling and storage of sets of data in non-volatile memory of the instrument, is an additional advantage in rural areas of third world countries where archiving of medical records is poor, and in industrialized countries, where physically and mentally incapacitated older generations cannot maintain records. The notion of an instrument for first order clinical diagnoses of chronic cases in particular, becomes important when patients and doctors are physically apart from each other and doctor needs to remain vigilant about early signal of deterioration of health of patients.

Electronics Industry
In last few years density of FPGAs and performance per watt have improved, which allows High Performance Computing (HPC) industry to increase and provide more functionalities in to hardware. Recently computing techniques like fuzzy logic and neural networks are gaining considerable importance in the field of automated medical diagnosis. Fuzzy logic is used in situations where approximate values of patient data are to be analyzed using linguistic variables [10,11]. Similarly neural networks are used in situations where the knowledge about the patient is stored in the form of numerical data sets [12–15]. A synergism of these two computational intelligence tools can lead to the formation of a hybrid system that is capable of learning from approximate knowledge and also utilize the acquired knowledge for futuristic reasoning [16–17].

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[2] D.M. Eddy, The challenge, Journal of American Medical Association 263 (1990) 287–290.
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[9] D. Hailey, P. Jacobs, Assessment of Telehealth Applications, Alberta Heritage Foundation for Medical Research, Edmonton (Alta), 1997.
[10] L.A. Zadeh, Fuzzy Sets, Information and Control 8 (1965) 338–353.
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[12] R.P. Lippmann, An introduction of computing with neural nets, IEEE ASSP Magazine (1992) 4–22.
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[14] B. Widrow, M.A. Lehr, 30 years of adaptive neural networks: perceptron, madaline and backpropagation, Proceedings of the IEEE Neural Networks Conference 78 (1990) 1415–1442.
[15] E.D. Ubeyli, Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals, Computers in Biology and Medicine
38 (1) (2008) 82–89.
[16] A. Sengur, An expert system based on principal component analysis, artificial immune system, fuzzy k-NN for diagnosis of valvular heart diseases, Computers in Biology and Medicine 38 (3) (2008).
[17] A. Konar, S. Pal, Modeling cognition with fuzzy neural nets, in: C.T. Leondes (Ed.), Fuzzy Theory Systems: Techniques and Applications, vol. 3, Academic Press, 1999.
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Bio-medical Sensors

Unread postby UCERD.COM » Mon Oct 07, 2013 2:54 pm

In-situ Sensor : 

good read
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