Autism Model Design

Techniques and technologies in the medical field constantly evolve and change. With such a huge increase in information, there is

After the release of ResearchKit, Medidata revealed an open- source connecter linking the Apple ResearchKit to the Medidata Clinical Cloud. This integration will further help to use the data that is collected through the ResearchKit to improve the study designs. “Medidata has developed a mechanism allowing study builders to map the data generated from ResearchKit apps to studies in Medidata Rave” (NASDAQ:MDSO, 2015). The connection of software will give birth to a new level of productivity; expanding the quality, study designs, management and reporting of the clinical trials. “With the use of semantic web technology, a researcher will be able to create a data file using tags that identified which data items identified the researchers, the inclusion criteria, the aim of the study, the baseline tests to be performed, the details of the treatments to be compared and so on. And such data can be analyzed

Medicine in the 21st Century has continued to push the boundaries of science to unthinkable limits, often fusing complex technological elements and concepts together to achieve great feats. Two major contributing factors that have increased the use of high-technology in modern day medicine are Evidence-based Medicine and Health-Informatics. One study completed at Ohio State University (2016) proved how beneficial combining high-technologies such as Health Informatics Tools (HIT) and Evidence-Based Medicine can be. "Achieving a vision of 'interventional informatics ' will requires us to re-think how we study HIT tools in order to generate the necessary evidence-base that can support and justify their use as a primary means of improving the human condition" (Embi et al, 2016). This synergistic effect is just one of many countless examples of the successful advancements in medicine today. These strings of successful advancements in medicine can almost certainly be linked to the progression of specific

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

The book The Personalized Medicine Revolution: How Diagnosing and Treating Disease are About to Change Forever is an interesting journey through the evolution of our scientific knowledge and subsequent technologies followed by a final foray into the most current accomplishments in personalized medicine and where it’s going.

Arden Syntax is a language for encoding medical knowledge bases as independent modules known as Medical Logic Modules (MLM), and has been used to generate clinical alerts, as well as implement practice guidelines and care plans [11]. Medical Logic Modules comprise of slots which are grouped into three categories: Maintenance, Library and Knowledge. Maintenance category specifies knowledge base maintenance and change control information; library category contains explanatory information, keywords, and links to other sources; and knowledge category houses the actual medical knowledge,

Minimal Clinical Important Difference (MCID) is the threshold value of a change which its definition for each outcome is critical when assessing side effects, costs, and efforts, but most importantly success of an intervention. Distribution-based MCID is a measure of variability such as the standard error of measurement (SEM), the standard deviation (SD), the effect size, or the minimum detectable change (MDC). Distribution based method do not address the question of clinical importance; this is done by an anchor-based methods that give a subjective estimation such as patient’s response (e.g., a global assessment rating in which the patients rate themselves to some extent as "better", "unchanged" or "worse")

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

In recent years, there has been an explosion of available data as the medical field has moved from paper-based to electronic health records (EHR), along with other big data sources such as digital imaging, genomics, proteomics, and metabolomics. To make this vast amount of data clinically useful and to support in-depth analysis to understand the molecular basis of diseases, methods and tools are required that accurately integrate and link -omics data with clinical information (1).

Online databases are a pool of scientific resources that are accessible through a client-server model, where a centralised server provides to individuals that request its services and resources. It usually acts similarly to a search engine. PubMed, Medical Literature Analysis and retrieval System Online (MEDLINE), and National Center for Biotechnology information (NCBI) are among the most widely used free online databases in many fields of life science. The amount and variety of information available in

Latest work in description logics provides various results to fulfill such a repeat of a reference ontology-based DMS. Indeed, modern ontological languages – like the W3C advices RDFS, OWL, and OWL2 – are actually XML-based phonological variants of well-known DLs. All those results consist in taking out a section from an existing ontological schema such that all the conditions respecting the relations of absorption for the application under development are captured in the module previous definitions of sections in the literature basically spot to the notion of (deductive) conventional extension of a schema or of uniform introduce of a schema, a.k.a. omitting about non-interesting relations of a schema. Exemplify those two conceptions for schemas written in DLs and debates their connection. Till now, conventional extension has been

Adding hypernyms, hyponyms and synonyms to the document term vector, would lead to revelations of hidden subjects in the context. So the documents with more common concepts and ideas have a greater chance to be clustered in the same group. For example, a document about "موسیقی" (music) may have some ideas in a more abstract subject like "هنر" (art). The next step after choosing an appropriate sense for an ambiguous term is adding the inclusion (ISA) and synonymy relationships of that sense into the vector. One should determine the radius of the hypernyms (parents) and the hyponyms (children) of the winner synset (sense). The evaluation results for inclusion of concepts with more

Ontology contains a set of concepts and relationship between concepts, and can be applied into information retrieval to deal with user queries.

The field of bioinformatics is the process of technology by the use of computers (BioPlanet, 2015). This field manages biological information (BioPlanet, 2015). We all use computers on a regular basis, but computers are capable of much more they can store biological information, gather this information, analyze and integrate genetic information as well (BioPlanet, 2015). All these things can be applied towards drug discoveries like gene- based development (BioPlanet, 2015). Bioinformatics just recently became more popular since the availability of genomic information (BioPlanet, 2015). Now, because of the popularity of bioinformatics it has become a very important part of biology in many areas (BMIRC, 2015). In a type of biology called molecular biology bioinformatics is used in the extraction of results and data (BMIRC, 2015). Take another field for example, genetics and genomics, it sequences genomes and observes their mutations (BMIRC, 2015). It also plays an important part in organizing biological and gene data (BMIRC, 2015). Both of these are examples of how bioinformatics is in a lot of areas of biology (BMIRC, 2015).The tools in bioinformatics help in the comparison of data such as genetic and genomic (BMIRC, 2015). It also helps you receive a better understanding of molecular biology (BMIRC, 2015). At a deeper level

WordNet is an enhanced dictionary in which words are classified based on their meanings. In WordNet, synonym words are grouped in a structure called synset. Each synset represents a separate concept. However, there are several differences between a WordNet and a dictionary. First, a WordNet not only connects lexical part of words, but also connects their concepts. Therefore, words located close to each other in the WordNet have also semantic proximity. Second, WordNet tags the semantic relation between words; while the classification of words in a classical dictionary is only based on their lexical similarity and it does not specify any semantic relation between words.