Medical Informatics: Preventive Medicine Applications via Telemedicine

Introduction

In the last decade, information and communication technologies (ICT) have seen enormous growth and have been introduced by various degrees into the medical environment. The extra processing power and facilities open up the scope for much more powerful processing and networking of medical applications. Communication networks are becoming increasingly large in size and heterogeneous in nature. Recent advantages in communication technologies have contributed to an explosion of new services directed at the medical environment. The general goal of using communication technologies in medical environments is to improve the overall quality of healthcare at an affordable cost. This requires close interaction between healthcare practitioners and information technologists to ensure that the proposed technologies satisfy current user’s needs and anticipate future ones. Appropriate application of ICT in primary healthcare will extend traditional diagnosis and patient management beyond the physician’s clinic into the everyday living environment (Pombortsis; 1998; Ruggiero et al., 1999; Lovell & Celler, 1999; Anantharaman & Han, 2001; Bliven et al., 2001; Güler & Übeyli, 2002; Übeyli, 2009).

Medical informatics is the development and assessment of methods and systems for the acquisition, processing and interpretation of patient data with the help of knowledge from scientific research. The focus are the patient and the process of care. The foundation for any medical decision support is the medical knowledge base which contains the necessary rules and facts. This knowledge needs to be acquired from information and data in the fields of interest, such as medicine. Three general methodologies to acquire this knowledge can be distinguished: traditional expert systems, evidence-based methods, statistical and artificial intelligence methods. The medical decision support system consists of differential diagnosis, computer-assisted instruction, consultation components and their subsystems. The differential diagnosis component contains three subsystems: artificial neural network (ANN) model, time series analysis and medical image analysis. ANNs are computational modeling tools that have recently emerged and found extensive acceptance in many disciplines for modeling complex real-world problems. ANNs produce complicated nonlinear models relating the inputs (the independent variables of a system) to the outputs (the dependent predictive variables). ANNs have been widely used for various tasks, such as pattern classification, time series prediction, nonlinear control, function approximation, telecommunications and biomedical (Haykin, 1994; Basheer and Hajmeer, 2000; Chaudhuri & Bhattacharya, 2000). Time series analysis is based on the extraction of information from medical signal data. Medical image analysis can be used for medical decision making. Important tools in modern decision-making, in any field, include those that allow the decision-maker to assign an object to an appropriate group, or classification (Anderson et al., 1997; Bellazzi et al., 2001; Bellazzi, 2003; Thornett, 2001; Imhoff, et al., 2001; Brown, 1998; Conner et al., 2000).