The human genome refers to the complete set of nucleic acid sequence usually encoded as DNA in each of the 23 pairs of chromosomes (Kia et al., 2017). It features both the protein coding and non-coding DNA. A genome sequence refers to the series of appearance of the DNA nucleotides. The genome sequencing process, therefore, refers to the act of trying to identify how these DNA nucleotides appear in a genome (Kia et al., 2017). A decade from now, the Clinical Decision Support System (CDSS) is expected to be influenced by cheap sequencing technologies which will use the whole genomic sequence in the place of the one million single nucleotide polymorphisms (Bush & Moore, 2012). Therefore, processes that can be presently handled by many employees of the CDSS such as data storage and manipulation, quality control and data analysis, will have to be done by computer science and bioinformatics experts.
The Boolean logic is basically a form of algebra whereby all values are reduced to their most basic forms which can be described as either true or false (Balbiani & Tinchev, 2014). It goes hand in hand with the binary numbering system where each bit can either have the value of 1 or 0. Hence, mathematical subjects of statement logic and predicate logic work together to bring about a formal definition of how statements can be used together to yield conclusions. For example, a statement may state; “Urine cultures exposing a colony count of over 10,000 are considered positive if obtained through the process of bladder catheterization. This patient’s urine culture indicates the presence of more than 10,000 E.coli colonies. Positive urine cultures mean that the patient should be treated for urinary tract infection” Without the use of any formal mathematical figure, one can easily conclude that the patient needs immediate treatment for UTI. This conclusion is easily obtained from the human intuition. Unfortunately, computers do not have this intuition. This is why they need to be programmed so as to enable them to offer statement conclusions. Here, deductive reasoning will be needed (Balbiani & Tinchev, 2014). There needs to be a major premise, a minor premise and the conclusion. The major premise is: “Urine cultures exposing a colony count of over 10,000 are considered positive if obtained through the process of bladder catheterization.” The minor premise, on the other hand, is: “This patient’s urine culture indicates the presence of more than 10,000 E.coli colonies.” Here, the major premise is represented as:
Over 10000 E. coli=UTI
UTI= High rate alarm
Over 10000 E. coli= High Rate Alarm
Logistic regression refers to the appropriate regression analysis that is required when there is a binary dependent variable. It is used to describe data and indicate the relationship between one dependent binary and other interdependent variables. It helps the CDS model to assist clinicians in decision making by estimating different odds of a situation or illness. Therefore, predictions will be based on this, making it easier determine the right outcome.
CDSS Intervention types include; a) Always involve the use of decision support when carrying out normal tasks of patient consultation and document notes. b) Retrieve preformed orders that are used to manage a specific disease, or for carrying out a particular procedure. c) Data review and use a lifesaving therapy when needed. d) assessment and understanding e) triggered by user task.
When using the intervention of assessment and understanding for a patient with UTI, a ‘more info’ button should be introduced in the electronic health records so that anyone following up on this condition will immediately learn more about it. The information source needs to feature evidence based knowledge with regards to the disease and treatment. (See image below)
The architecture can be used for various reasons. First, when a different patients shows up with similar symptoms to the present patient, the same information may be used as guidance. Second, a patient may present to the clinic with different problems, including UTI. This architecture will be used to establish contraindicated drugs so as to prevent complications.
Balbiani, P., & Tinchev, T. (2014). Definability and Canonicity for Boolean Logic with a Binary Relation. Fundamenta Informaticae, 129(4), 301-327. doi:10.3233/FI-2014-973
Bush, W. S., & Moore, J. H. (2012). Chapter 11: Genome-Wide Association Studies. PLoS Computational Biology, 8(12), e1002822. http://doi.org/10.1371/journal.pcbi.1002822
Kia, A., Gloeckner, C., Osothprarop, T., Gormley, N., Bomati, E., Stephenson, M., & … He, M. M. (2017). Improved genome sequencing using an engineered transposase. BMC Biotechnology, 171-10. doi:10.1186/s12896-016-0326-1