ACUTA eNews March 1988, Vol. 17, No. 3

In This Issue President\u27s Message Party Line 17th Annual Summer Conference Potpourri From the Board Plan Now For San Diego The University of British Columbia The Other Side of T-

Automatically selecting patients for clinical trials with justifications

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

Telco Network Inventory Validation with NoHR

Network database inventory is a critical tool for the operations of any telecommunication company, by supporting network configuration and maintenance, as well as troubleshooting of network incidents. Whereas an incorrect inventory can often lead to severe implications and financial losses, the sheer size of a telecommunication network, the number of equipment involved, and other operational constraints, often lead to outdated inconsistent inventories, which are usually validated and updated by hand, during change management processes – a time-consuming task highly prone to human error. In this paper, we describe a solution to automate the validation of network inventories within the context of a multinational telecommunication company, with operations in several different countries, using NoHR, a reasoner that allows the user to query (hybrid) knowledge bases composed of ontologies and non-monotonic rules, both of which are necessary to perform the kind of reasoning required by this task. In addition, to address severe performance issues – essentially in terms of memory – resulting from NoHR v3.0’s need to pre-process the entire database into OWL assertions or rule facts, in this paper, we also present v4.0 of NoHR, which extends NoHR v3.0 with native support for Databases, solving not only the memory consumption problems, but also improving the average reasoning times

Telco Network Inventory Validation with NoHR

Network database inventory is a critical tool for the operations of any telecommunication company, by supporting network configuration and maintenance, as well as troubleshooting of network incidents. Whereas an incorrect inventory can often lead to severe implications and financial losses, the sheer size of a telecommunication network, the number of equipment involved, and other operational constraints, often lead to outdated inconsistent inventories, which are usually validated and updated by hand, during change management processes – a time-consuming task highly prone to human error. In this paper, we describe a solution to automate the validation of network inventories within the context of a multinational telecommunication company, with operations in several different countries, using NoHR, a reasoner that allows the user to query (hybrid) knowledge bases composed of ontologies and non-monotonic rules, both of which are necessary to perform the kind of reasoning required by this task. In addition, to address severe performance issues – essentially in terms of memory – resulting from NoHR v3.0’s need to pre-process the entire database into OWL assertions or rule facts, in this paper, we also present v4.0 of NoHR, which extends NoHR v3.0 with native support for Databases, solving not only the memory consumption problems, but also improving the average reasoning times