Ontologies Used In Paper-Omics

Phenotype: The physical appearance of a trait in an organism. The phenotype of the above chicken is that it is black. A phenotype could be that a person has blue eyes, or curly hair.

After decades of paper based medical records, a new type of record keeping has surfaced - the Electronic Health Record (EHR). EHR is an electronic or digital format concept of an individual’s past and present medical history. It is the principle storage place for data and information about the health care services provided to an individual patient. It is maintained by a provider over time and capable of being shared across different healthcare settings by network-connected information systems. Such records may include key administrative and clinical data relevant to that persons care under a particular provider. Examples of such records may include: demographics, physician notes, problems or injuries, medications and allergies, vital

The knowledge base for DXplain contains more than 65,000 relationships between diseases and patient descriptors (or clinical manifestations). The format for these relationships and the data structures are similar to the format and data structures used in the INTERNEST/QMR system except that in DXplain terms cannot only support a given diagnosis, but can also contradict other diagnoses. The relationships in DXplain are concerned with three different elements: 1) term importance, 2) term frequency and 3) term-evoking power. Term importance is used to express how significant the particular term is in indicating the presence of disease. A high term importance is given to findings that can be identified with high reliability or are rarely found

Health information technology (HIT) is revolutionizing the way we interact with health-related data. One example of this is the obvious rise in

NIH Public Access published an article about SNOMED CT 's RF2. SNOMED is complex and extensive computer information that allows common medical to be captured, shared, and aggregating health data. SNOMED was established in 2002 and was research up until 2010. During that time there were many problems that arise. Therefore, in 2010 the International Health Terminology Standards Development Organization (IHTSDO) that implemented a new format called RF2. At first glance, RF2 seems to be a cosmetic change; however, it holds great promise for the future because of its flexibility and to deal with a number of issues concerning the ontological underpinnings.

The passage of the American Recovery and Reinvestment Act encouraged and mandated the use of health information exchange (HIE) technology in the healthcare industry. The time had finally come to enter into the electronic age, and learn how to integrate electronic health records (EHRs) into their environment. Evolution and revolution are never easy, and several issues will arise during the transition. As EHR utilization spread through healthcare organizations, problems with interoperability became evident. How could healthcare organizations successfully achieve interoperability, and collect consistent patient data? A data dictionary may be the key to unlocking an accurate and efficient HIE.

Worldwide use of computer technology in medicine began in the early 1950s with the rise of the computers. In 1949, Gustav Wagner established the first professional organization for informatics in Germany. Medical informatics research units began to appear during the 1970s in Poland and in the U.S. Since then the development of high-quality health informatics research, education and infrastructure has been a goal of the U.S. and the European Union. (NYU graduate training program, 2010) Changes in the healthcare environment produced fundamental shifts in the delivery of healthcare. The altering landscape of healthcare is creating a huge demand for health data analytics. The growth and maturity of healthcare informatics over the past decade has been a prime catalyst in positioning the healthcare industry for the changes posed by reform measures. By understanding the process of analytics, clinical informatics specialists say healthcare providers have the insight necessary to make the process adjustments in the future.(Riskin, 2013)

HC1: Personalized medicine is a relatively new practice that involves using a person’s genetics to make medical decisions about which direction to take in terms of disease prevention, treatment, and diagnosis (“Personalized medicine,” 2015). The practice of personalized medicine allows doctors to predict which diseases a certain individual may be susceptible to so that proper treatment less likely to harm the patient can be ensued. This could potentially be an incredible breakthrough in the field of science and medicine (“What is personalized”, n.d.).

When I first enrolled in BMI 5300: Introduction to Biomedical Informatics class, I was very eager and excited to learn the role of biomedical informatics in healthcare organizations and the importance of data capture and analysis in improving public health. One of the major misconceptions I had was that this course would not cover wide range of factors influencing the biomedical informatics field. Much to my surprise, the course provided a comprehensive analysis of factors related to biomedical informatics, including but not limited to EHR systems, interfaces, Health Information Exchanges, Meaningful Use (MU) requirements, and controlled medical vocabularies such as Logical Observation Identifiers Names and

Decision support systems are ideally interactive systems that allow the decision making physician to come to the conclusion based on a host of information pulled from data bases, personal knowledge, predesigned modals etc. Decision support systems have many benefits such as; patient-time efficiency, speed up process of decision making, promotes learning and training, reveals new approaches in thought process, generates new evidence in support of a decision and encourages exploration and discovery of the decision maker (Bosworth, York, Kotansky, & Berman, 2011). Although these systems require end user expertise, correct inputs and appropriate modals, they also require vast and exstenive information. Immunoinformatics are used to compile vast amounts of data for the immunology field (De Groot A. , Immunomics: discovering new targets for vaccines and therapeutics , 2006). This data includes genetic mapping, protein structures, cytometry data and many other data pools needed by immunologists to make correct decisions. Immunoinformatics face to challenge of compiling this enormous amounts of data in an organised and correct way. This data needs to be mapped into correct diagnostic modals for the physician to use in their decision support system (Barh, Misra, & Kumar, 2010). This data leads to new ways of hypothesis testing for immune responses

In health care, patients’ lives are in the hands of the health care practitioners, health care organizations, insurance companies, and to some degree, even health care technology. The growth and future implications of evidence-based medicine (EBM) through improvement of technology in health care are important today, because health care practitioners and organizations want to ultimately decrease cost, improve quality of care, and increase access to health care (Glandon, Smaltz, & Slovensky, 2014, p. 28). One way to achieve these goals is through the implementation and improvement of EBM and interoperability which will enhance the efficiency of work production resulting in these positive outcomes. According to Glandon, Smaltz, and Slovensky (2014), EBM is an “information management and learning strategy that seeks to integrate clinical expertise with the best evidence available to make effective clinical decisions that will ultimately improve patient care,” (p. 6). “Interoperability is the ability of different information and communications technology systems and software applications to communicate, to exchange data accurately, effectively, and consistently, and to use information that has been exchanged,” (Iroju, Soriyan, Gambo, & Olaleke, 2013, para. 1). Without interoperability and EBM, fundamental data and information such as patient records cannot be easily shared across and within enterprises having a direct impact on the quality of care. It

Genes come in different varieties, called alleles. Somatic cells contain two alleles for every gene, with one allele provided by each parent of an organism. Genotype refers to the information contained in an organisms DNA, or genetic material. Its phenotype is the physical

While Ashburner et al.[6] show the success of the Gene Ontology in how a controlled and properly curated ontology can benefit and extend research in medicine. In Scheuermann et al.[7] represent Ontology for General Medical Science demonstrate entities in the domain of medicine and disease and addresses the need to integrate biomedical data. The description logic as we use in this paper is expressive enough for defining the relevant concepts in enough detail, but not too expressive to make reasoning infeasible. The Gomes et al.[8] describe structure of description logics and its approach in the cardiology medical environment, and we compare the use of description logics in the pathology environment by using a practical model of description logics use in terms of diseases related to the circulatory system of the human

According to biology-online.org, genotypes are defined as, “The entire set of genes in an organism… A set of alleles that determines the expression of a particular characteristic or trait.” A genotype is an organism’s Genetic make-up. A human’s genetic code is unique to each individual, a blueprint that is provided by the parents. “The genotype of an organism is the class to which that organism belongs as determined by the description of the actual physical material made up of DNA that was passed to the organism by its parents

Complex diseases are caused by multiple genetic and environmental factors working in combination with each other and thus, it is difficult to characterise the contribution of any one factor to the disease1. However, the widespread adoption of genome-wide association studies (GWAS) has greatly accelerated the rate at which these factors are discovered and characterised. These studies genotype individuals with different phenotypes (for example, those who are affected or unaffected by a complex disease) at hundreds of thousands of single nucleotide polymorphisms (SNPs). If a SNP is statistically more common in one phenotypic group then it is said to be associated with that phenotype2.