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 theraphy 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 theraphy,
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 theraphy, 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 theraphy
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 theraphy 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 theraphy,
including diet, drug treatment, or radiotheraphy 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 theraphy 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 theraphy 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 enable flexibility in access to
information through the World Wide Web or comprehensive knowledge bases.
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:
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 theraphy
for malaria, typhoid fever and pneumonia.
The theraphy 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.
DiagnosticCriteria: This term designates the
specific combination of signs, symptoms, and test results that the clinician
uses to attempt to determine the correct diagnosis.
Theraphy
critiquing and consulting: This function of a clinician implies assessing
of the theraphy 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.