Abstract
This project describes the “Design and Implementation of a medical diagnostic systemâ€ÂÂ. The main objective of this project was to implement a medical diagnostic system that will aid clinicians in diagnostic procedures. The researcher’s motivation for choosing this topic was to help develop Security System of the medical diagnostic system. The researcher adopted Object Oriented Analysis and Design Methodology (OOADM) for this study. Interviews, journals and observation methods were used as data collection methods in order to achieve a success. The system was successfully developed, carefully tested and implemented using HTML and Visual Basic 6.0. Finally a medical diagnostic system which is able to offer information on effective disease prevention was eventually achieved.
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
According to Frost
et al. (2009) medical diagnosis, (often
simply termed diagnosis) refers both to the process of attempting to determine
or identifying a possible disease or disorder to the opinion reached by this
process. A diagnosis in the sense of diagnostic procedure can be regarded as an
attempt at classifying an individual’s health condition into separate and
distinct categories that allow medical decisions about treatment and prognosis to
be made. Subsequently, a diagnostic opinion is often described in terms of a
disease or other conditions. In the medical diagnostic system procedures,
elucidation of the etiology of the disease or conditions of interest, that is,
what caused the disease or condition and its origin is not entirely necessary.
Such elucidation can be useful to optimize treatment, further specify the
prognosis or prevent recurrence of the disease or condition in the future
(Frost
et al. 2009).
Clinical decision support systems
(CDSS) are interactive computer programs designed to assist healthcare
professionals such as physicians, physical therapists, optometrists, healthcare
scientists, dentists, pediatrists, nurse practitioners or physical
assistants with decision making skills. The clinician interacts with the
software utilizing both the clinician’s knowledge and the software to make a
better analysis of the patient’s data than neither humans nor software could
make on their own (Coulter
et al.
2010).
Typically, the system makes suggestions
for the clinician to look through and picks useful information that removes
erroneous suggestions. To diagnose a disease, a physician is usually based on
the clinical history and physical examination of the patient, visual inspection
of medical images, as well as the results of laboratory tests. In some cases,
confirmation of the diagnosis is particularly difficult because it requires
specialization and experience, or even the application of interventional
methodologies (e.g., biopsy). Interpretation of medical images (e.g., Computed
Tomography, Magnetic Resonance Imaging, Ultrasound, etc.) usually performed by
radiologists, is often limited due to the non-systematic search patterns of
humans, the presence of structure noise (camouflaging normal anatomical
background) in the image, and the presentation of complex disease states
requiring the integration of vast amounts of image data and clinical
information. Computer-Aided Diagnosis (CAD), defined as a diagnosis made by a
physician who uses the output from a computerized analysis of medical data as a
second opinion‖ in detecting lesions, assessing disease severity, and making
diagnostic decisions, is expected to enhance the diagnostic capabilities of
physicians and reduce the time required for accurate diagnosis. With CAD, the
final diagnosis is made by the physician (Negnevitsky, 2005).
The first CAD systems were developed
in the early 1950s and were based on production rules and decision frames. More
complex systems were later developed, including blackboard systems to extract a
decision, Bayes models and artificial neural networks (ANNs) (Smith, 2009).
Recently, a number of CAD systems have been implemented to address a number of
diagnostic problems. CAD systems are usually based on biosignals, including the
electrocardiogram (ECG), electroencephalogram (EEG), and so on or medical
images from a number of modalities, including radiography, computed tomography,
magnetic resonance imaging, ultrasound imaging, and so on. In therapy, the
selection of the optimal therapeutic scheme for a specific patient is a complex
procedure that requires sound judgement based on clinical expertise, and
knowledge of patient values and preferences, in addition to evidence from
research. Usually, the procedure for the selection of the therapeutic scheme is
enhanced by the use of simple statistical tools applied to empirical data. In
general, decision making about therapy is typically based on recent and older
information about the patient and the disease, whereas information or
prediction about the potential evolution of the specific patient disease or
response to therapy is not available. Recent advances in hardware and software
allow the development of modern Therapeutic Decision Support (TDS) systems,
which make use of advanced simulation techniques and available patient data to
optimize and individualize patient treatment, including diet, drug treatment,
or radiotherapy treatment (Smith, 2009).
In addition to this, CDS systems may
be used to generate warning messages in unsafe situations, provide information
about abnormal values of laboratory tests, present complex research results,
and predict morbidity and mortality based on epidemiological data.