Home Project-material DESIGN AND IMPLEMENTATION OF DECISION SUPPORT SYSTEM FOR MEDICAL DIAGNOSIS

DESIGN AND IMPLEMENTATION OF DECISION SUPPORT SYSTEM FOR MEDICAL DIAGNOSIS

Dept: INFORMATION TECHNOLOGY File: Word(doc) Chapters: 1-5 Views: 8

Abstract

This research aims at the design and implementation of a decision support system for the diagnoses of kidney stone to assist physicians make better decisions. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion and this makes the assembly and interpretation of results related to kidney stone challenging. We used the Object-Oriented Analysis and Design Methodology (OOADM) and Visual BASIC.NET as a language of implementation. The new system collects comprehensive information about kidney stone disease from a group of experts and a rule-based system is formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. The medical diagnosis rules were formulated and applied by developing a new system. We defined a set of symptoms related to the set of considered disease. A group of patients were used to validate the effectiveness of the system. The results show that the system was cap
1.1     BACKGROUND OF STUDY

Blood which accounts for about 8 percent of your normal body weight, plays an important role in how your body functions. As the blood circulates throughout the vascular system, it supplies all other organs with oxygen, nutrients, hormones and antibodies. Blood is made of an almost equal mix of plasma (the liquid that transports cells, waste and nutrients, among other things) and blood cells (red blood cells, white blood cells and platelets).

When cancer occurs in the blood, it is usually the result of an abnormal and excessive reproduction of white blood cells. Blood cancers account for about 10 percent of all diagnosed cancers in the U.S. each year. Blood cancers (including leukemia, lymphoma and myeloma) are more common in men than women. Childhood leukemia accounts for about 30 percent of all cancers in children.

Some blood cancers may cause symptoms such as severe fatigue, weight loss, night sweats, or lymph node swelling. Other blood cancers may show no symptoms and slowly progress over years. Treatments for blood cancers also vary, ranging from active surveillance without cancer-directed therapy to standard cancer treatments including immunotherapies, chemotherapies and targeted agents. With over 100 different types of blood cancers now recognized, it is important to have an accurate diagnosis prior to deciding on treatment.

Cancer is the uncontrolled growth of abnormal cells in the body. Cancerous cells are also called malignant cells. Cells are the building blocks of living things. Cancer grows out of normal cells in the body. Normal cells multiply when the body needs them, and die when the body doesn’t need them. Cancer appears to occur when the growth of cells in the body is out of control and cells divide too quickly. It can also occur when cells forget how to die. There are many different kinds of cancers. Cancer can develop in almost any organ or tissue, such as the lung, colon, breast, skin, bones, or nerve tissue (Patra, 2010).

 

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 patients’ data than neither human nor software could make on their own.

Typically, the system makes suggestions for the clinician to look through and then 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 in the image and the presentation of complex disease states requiring the integration of vast amount of image and clinical information.

 

Computed aided diagnosis (CAD) defined as a diagnosis made by a physician who uses the output from a computerized analysis of a medical data as a second opinion indicating lesions, accessing 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.

In this research, expert system was used to develop models for the rapid diagnosis of blood cancer for medical records of patients suffering from this condition. First, a data model was developed for symptoms and diagnosis of blood cancer after a series of discussion with medical experts in the field on methods of diagnosis, then expert system models were developed for the diagnosis with expert advice on educating patients on the possible causes of blood cancer.



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