CHAPTER ONE

INTRODUCTION
1.0 BACKGROUND OF STUDY

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.

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.

Typically, the system makes suggestions for the clinician to look through and
the he picks useful information and 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.

The first CAD systems were developed in the early 1950s and were based on
production rules (Shortliffe, 1976) and decision frames (Engelmore & Morgan,
1988). More complex systems were later developed, including blackboard
systems (Engelmore & Morgan, 1988) to extract a decision, Bayes models
(Spiegelhalter, Myles, Jones, & Abrams, 1999) and artificial neural networks
(ANNs) (Haykin, 1999). 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.

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.

1.2 STATEMENT OF THE PROBLEM
Disease diagnosis and treatment constitute the major work of physicians. Some
of the time, diagnosis is wrongly done leading to error in drug prescription and
further complications in the patient’s health. It has also been noticed that much
time is spent in physical examination and interview of patients before treatment
commences. The clinical decision support system (CDSS) shall address these
problems by effectively providing quality diagnosis in real-time.

1.3 OBJECTIVES OF THE STUDY
To develop modern interactive diagnostic software that will aid clinicians
in diagnostic procedures.

• To offer prescription of medication.
• To enable flexibility in access to information through the World Wide
• Web or comprehensive knowledge bases.
• To offer information on effective disease prevention.
• To provide for real-time overall effective, efficient and accurate service
delivery by clinicians in line with global medical health standards.

1.4 SIGNIFICANCE OF STUDY
Advances in the areas of computer science and artificial intelligence have
allowed for development of computer systems that support clinical diagnostic or
therapeutic decisions based on individualized patient data. Clinical decision
support (CDS) systems aim to codify and strategically manage biomedical
knowledge to handle challenges in clinical practice using mathematical
modeling tools, medical data processing techniques and artificial intelligence
(A.I.) methods.
Its significance is also seen in its ability to:
Provide diagnostic support and model the possibility of occurrence of
various diseases or the efficiency of alternative therapeutic schemes.
Reduce the potential for harmful drug interactions, prescription errors and
adverse drug reactions.
Enable clinicians report adverse drug reactions to the relevant authorities.
Promote better patient care by enhancing collaboration between
physicians and pharmacists.

1.5 SCOPE OF THE STUDY
Due to the fact that it is difficult to develop an expert system for diagnosing all
diseases at a time, financial and time constraints, this research is limited to
medical diagnosis and treatment for malaria, typhoid fever and pneumonia.
The therapy covers severe and uncomplicated cases of the treatment of extreme
or severe associated cases in patients such as cerebral malaria which causes
insanity, blondness, asthma, tuberculosis and so on.
The study will also involve method(s) of diagnosis especially the patient
history, physical examination and request for clinical laboratory test but will not
go into how these tests are carried out.
Rather, it will only make use of the laboratory and treatment.

1.6 LIMITATIONS OF THE STUDY
In the course of this study, a major constraint experienced was that of time
factor and insufficient finance. Others include the inevitability of human error
and bias as some information were obtained via interpersonal interactions,
interviews and research, making some inconsistent with existing realities or
outrightly incorrect.
Great pains were however taken to ensure that these limitations are at their very
minimum and less impactful on the outcome of the work.

1.7 DEFINITION OF RELATED TERMS
Here, the researcher shall try as much as possible to explain certain technical
terms used during the course of his study.
Prognosis: This is a medical opinion as to the likely outcome of a disease.
Etiology: This is the branch of medicine that investigates the causes and origin
of diseases.
Diagnostic Criteria: This term designates the specific combination of signs,
symptoms, and test results that the clinician uses to attempt to determine the
correct diagnosis.
Therapy critiquing and consulting: This function of a clinician implies
assessing of the therapy looking for inconsistencies, errors, cross-references for
drug interactions and prevents prescribing of allergenic drugs.
Allergen: A substance that causes an allergy.
Epidemiology: The scientific and medical study of the causes and transmission
of disease within a population.