1,413 research outputs found

    Automating the integration of clinical studies into medical ontologies

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    A popular approach to knowledge extraction from clinical databases is to first define an ontology of the concepts one wishes to model and subsequently, use these concepts to test various hypotheses and make predictions about a person’s future health and wellbeing. The challenge for medical experts is in the time taken to map between their concepts/hypotheses and information contained within clinical studies. Presently, most of this work is performed manually. We have developed a method to generate links between Risk Factors in a medical ontology and the questions and result data in longitudinal studies. This can then be exploited to express complex queries based on domain concepts, to extract knowledge from external studies

    Mapping longitudinal studies to risk factors in an ontology for dementia

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    A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces

    Concept Based Dynamic Ontology Creation for Job Recommendation System

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    AbstractThe basis of our research is to construct a job recommendation system to the job seekers by collecting the job portals data. Due to huge amounts of the data in job portals the employers are facing difficulty in the identification of right candidate for the required skill and experience. The job seekers are also facing the problem of getting the suitability of the job based on their skill and experience. The knowledge acquisition based on the requirements is very difficult in case of huge amounts of the data sources. In fact classical development of domain ontology is typically entirely based on strong human participation. It does not adequately fit new applications requirements, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that human cannot achieve alone. The main focus of our work is to generate a job recommendation system with the details of job by taking account into the data posted in the web sites and data from the job seekers by the creation of dynamic ontology. We strongly believe that our system will give the best outcome in case of suitable job recommendation for both employers and job seekers without spending much time. To achieve this first we have extracted the data from various web pages and stored the collected data into .csv files. In the second stage the stored input files are used by the similarity measure and ontology creation module by generating the corresponding Web Ontology Language (.owl) file. The third stage is creating the ontology with the generated .owl by using protégé tool

    Automatically selecting patients for clinical trials with justifications

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    Clinical trials are human research studies that are used to evaluate the effectiveness of a surgical, medical, or behavioral intervention. They have been widely used by researchers to determine whether a new treatment, such as a new medication, is safe and effective in humans. A clinical trial is frequently performed to determine whether a new treatment is more successful than the current treatment or has less harmful side effects. However, clinical trials have a high failure rate. One method applied is to find patients based on patient records. Unfortunately, this is a difficult process. This is because this process is typically performed manually, making it time-consuming and error-prone. Consequently, clinical trial deadlines are often missed, and studies do not move forward. Time can be a determining factor for success. Therefore, it would be advantageous to have automatic support in this process. Since it is also important to be able to validate whether the patients were selected correctly for the trial, avoiding eventual health problems, it would be important to have a mechanism to present justifications for the selected patients. In this dissertation, we present one possible solution to solve the problem of patient selection for clinical trials. We developed the necessary algorithms and created a simple and intuitive web application that features the selection of patients for clinical trials automatically. This was achieved by combining knowledge expressed in different formalisms. We integrated medical knowledge using ontologies, with criteria that were expressed using nonmonotonic rules. To address the validation procedure automatically, we developed a mechanism that generates the justifications for each selection together with the results of the patients who were selected. In the end, it is expected that a user can easily enter a set of trial criteria, and the application will generate the results of the selected patients and their respective justifications, based on the criteria inserted, medical information and a database of patient information.Os ensaios clínicos são estudos de pesquisa em humanos, utilizados para avaliar a eficácia de uma intervenção cirúrgica, médica ou comportamental. Estes estudos, têm sido amplamente utilizados pelos investigadores para determinar se um novo tratamento, como é o caso de um novo medicamento, é seguro e eficaz em humanos. Um ensaio clínico é realizado frequentemente, para determinar se um novo tratamento tem mais sucesso do que o tratamento atual ou se tem menos efeitos colaterais prejudiciais. No entanto, os ensaios clínicos têm uma taxa de insucesso alta. Um método aplicado é encontrar pacientes com base em registos. Infelizmente, este é um processo difícil. Isto deve-se ao facto deste processo ser normalmente realizado à mão, o que o torna demorado e propenso a erros. Consequentemente, o prazo dos ensaios clínicos é muitas vezes ultrapassado e os estudos acabam por não avançar. O tempo pode ser por vezes um fator determinante para o sucesso. Seria então vantajoso ter algum apoio automático neste processo. Visto que também seria importante validar se os pacientes foram selecionados corretamente para o ensaio, evitando até eventuais problemas de saúde, seria importante ter um mecanismo que apresente justificações para os pacientes selecionados. Nesta dissertação, apresentamos uma possível solução para resolver o problema da seleção de pacientes para ensaios clínicos, através da criação de uma aplicação web, intuitiva e fácil de utilizar, que apresenta a seleção de pacientes para ensaios clínicos de forma automática. Isto foi alcançado através da combinação de conhecimento expresso em diferentes formalismos. Integrámos o conhecimento médico usando ontologias, com os critérios que serão expressos usando regras não monotónicas. Para tratar do processo de validação, desenvolvemos um mecanismo que gera justificações para cada seleção juntamente com os resultados dos pacientes selecionados. No final, é esperado que o utilizador consiga inserir facilmente um conjunto de critérios de seleção, e a aplicação irá gerar os resultados dos pacientes selecionados e as respetivas justificações, com base nos critérios inseridos, informações médicas e uma base de dados com informações dos pacientes
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