GBIPM is a medical model that employs individuals genetic information in determining measures to prevent or treat certain illnesses.
It emerged as a result of the Genome Project that was meant to evaluate the genetic sequence by identifying genes that cause various illnesses. People usually have different genetic makeup. For this reason GBIPM is rapidly developing to improve personal health care. This model accomplishes its purpose of influencing decision making concerning individualized health care. This is attributed to the fact that it employs genetic information of individuals. As mentioned earlier individuals have different genetic profiles. Therefore individuals experience different health problems. Despite other contributing factors such as environment genetics play a major role in individuals health issues. In this respect prevention and treatment measures should be precisely tailored for individuals. This has been achieved through several dimensions that if emphasized will continue to shape the direction of public health (Willard and Ginsburg 2009).
In the first place there is the identification and development of predictive and preventive medicine. Predictive medicine deals with prediction about the likelihood of a disease. On the other hand preventive medicine is concerned with preventing the disease. Genomic profiles can be prepared in advance by individuals undergoing specialized tests to provide their genetic information. For instance if the genomic profile of an individual predicts the likelihood of a certain disease happening like the heart disease the medical practitioner can initiate preventive measures to prevent or reduce the severity of the disease respectively (Willard and Ginsburg 2009).
Another significant dimension that GBIPM has been using can be seen in the selection of treatment and medication for individuals. This has been successful in determining of treatments and medications for various diseases especially cancer. Furthermore in prevention prediction is necessary during the treatment of a disease. It can help to determine the disease cause and thus assist in selecting the appropriate treatment and medication for the disease. For example when one has cancer genetic information can be of vital importance in determining of medication. Cancer medication responds differently based on genetic variation. In addition genetic variation can also be used to predict whether diseases such as cancer will spread to other organs of the body. This information can have a direct influence on how to treat the disease as well as improve the patient outcomes (Willard and Ginsburg 2009).
As earlier mentioned earlier apart from genetic factors there are other factors that contribute to causing health issues among individuals. However these factors such as environment interact with the genetic factors in one way or another. Gene to gene and gene to environment interactions have taken a dominant position in epidemiology studies regarding disease causes and outcomes. Research has proved that the occurrence of any disease is attributed to genetic factors and other factors at the same time. Molecular and Genetic epidemiology seeks to evaluate ways in which genetic factors interact with various factors to contribute to human carcinogens that trigger heath issues within individuals. In the first way it establishes the correlation between genes and the disease or disorder. Secondly it establishes the magnitude of genetic impact with respect to other risk factors to the disorder. Finally it identifies the genes that make up the genetic component. Molecular epidemiology measures exposures that relate to certain substances. It also evaluates characteristics of the genotypes using precise markers to identify disease categories. On the other hand genetic epidemiology is similar to molecular epidemiology. However it concentrates on the causes attributed to inheritance. It incorporates molecular biology into genetic research and this has enabled medical practitioners to understand the nature and risk factors of inherited diseases. In turn this has ensured the development of effective medical prevention and treatment methods (Friis 2010).
Expert systems, neural networks, and genetic algorithms
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