Knowledge discovery methodology for medical reports

Medical reports contain valuable information, not only for the patient that waits for the results but also the latent knowledge that is possible to extract from them. The recent introduction of standard structured formats like the Digital Imaging and Communications in Medicine Structured Report and the Clinical Document Architecture Health Level Seven provide an efficient generation, distribution, and management mechanism. Also, they provide an intuitive and effective manner of information representation, unlike the traditional plain text format. In this paper we present a knowledge discovery methodology for structured report interchange based on plain text medical reports using YALE, a leading open-source data mining tool and Open-ESB platform that provides conversion, parsing, different protocols and message formats interchange capabilities.Centro de Imagiologia da Trindade (CIT

Application of Formal Grammar in Text Mining and Construction of an Ontology

This work describes an investigation of formal grammar with application to text mining. It is an important area since text is the most widespread type of data and it contains a lot of potentially useful information. Unstructured nature of text requires other methods for its processing, in contrast to other types of data mining. In this work, the authors propose an original approach to text mining by making a parse tree for each sentence using regular grammar and creating an ontology and provide a demonstration of this system being implemented in a constrained scenario. This ontology can be used for different tasks, ranging from expert systems to automatic machine translation. The ontology is a network consisting of concepts linked by relations. The authors developed a new system to implement proposed approach working in different languages

An Artificial Intelligence Application in Health Developed on Covid-19 Documents

With the developments in computer science, the concept of artificial intelligence has appeared more frequently in recent years. The concept of artificial intelligence, which is basically defined as computers (machines) thinking like people and making decisions, has become very popular today. Artificial intelligence is used in many fields, especially computer science, education, law, trade, tourism and economy. The health sector is one of these areas. The importance of the applications developed in health sciences has emerged once again, especially during the pandemic process. The development of systems that help reduce the workload of healthcare professionals and make decisions by processing medical data is also an important and real problem that can be solved with artificial intelligence. In this study, natural language processing which is one of the main study subjects of artificial intelligence, has been developed a system that automatically determines the concepts such as disease, medication and treatment on medical data with artificial intelligence by the system. During the experimental studies, it was observed that 91% accurate estimation was made with the model developed. For this study, a Turkish dataset was created by scanning medical articles and studies related to Covid-19 disease.Keywords: Artificial Intelligence, Turkish natural language processing, Name entity recognition, Covid-19DOI: 10.7176/JHMN/75-0

Sistema de Sugestões Sensível ao Contexto

Over the last few years, pervasive systems have experienced some interesting development. Nevertheless, human-human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this dissertation, we have developed a pervasive system - named ConversationaL Aware Suggestion SYstem (CLASSY) - which is aware of the conversational context and suggests the users potentially useful documents or that, somehow, save time executing a specific task. We have also proposed two different approaches - the Neighborhood one, that uses semantic similarity, based on proximity data in order to classify the relationship between tokens; and the Reinforcement Learning one, that uses implicit feedback associated with each suggestion as a source of knowledge that can be used to improve the system's performance over time. The conducted tests showed that these two approaches not only enhanced the pervasive behavior of the system, but also increased its global performance. A case study regarding the importance of feedback on context-limited environments was also carried out, whose results showed that it is still a useful source of knowledge regardless the conversational environment's characteristics.Ao longo dos últimos anos, os sistemas pervasivos têm sido fonte de um grande desenvolvimento. Contudo, as interações humano-humano também podem tirar vantagem deste tipo de sistemas recorrendo à sua capacidade para entender o ambiente que o rodeia. Nesta dissertação, foi desenvolvido um sistema pervasivo - chamado Sistema de Sugestões Sensível ao Contexto (CLASSY) - que está consciente dos vários contextos conversacionais e que sugere documentos considerados potencialmente úteis para os utilizadores ou que, de alguma forma, poupam tempo na execução de uma tarefa específica. Foram também propostas duas aproximações diferentes - a de vizinhança, que usa similaridade semântica, baseando-se em proximidades de forma a classificar relações entre palavras; e a de Aprendizagem por Reforço, que usa feedback implícito dos utilizadores associado a cada sugestão, como fonte de conhecimento que pode ser utilizado para melhorar a performance do sistema ao longo do tempo. Os testes realizados mostraram que as aproximações acima referidas melhoraram não só o comportamento pervasivo do sistema, mas também a sua performance global. Foi, ainda, analisado um caso de estudo referente à importância de feedback em ambientes com contexto limitado, onde os resultados mostraram que o mesmo continua a ser uma importante fonte de conhecimento, independentemente das características do ambiente conversacional.Mestrado em Engenharia de Computadores e Telemátic

Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images

Ph.DDOCTOR OF PHILOSOPH

A medical ultrasound reporting system based on domain ontology

Ultrasound reports are produced in different ways by radiologists. These variations in reporting style could impact on the value of the report and the way it is interpreted, which in turn may have implications for patients’ management and decision making. As the images produced will not give the whole view of the examination, it is vital that a high quality and standardised ultrasound report is produced. In addition to their medical value, ultrasound reports contain a lot of important information that can be very useful in research and education. Reports can contain a variety of terms or heterogeneous terminologies used for describing similar findings. This research project aims to develop a medical ultrasound reporting system that uses domain ontology as its knowledge base to support the generation of standardised reports as well as Rhetorical Structure Theory (RST) to transform free text reports to the preferred structured and standardised format. The domain ontology will specifically focus on abdominal ultrasound scanning which includes both the anatomy and pathology of the organs in the abdominal area. The ontology was developed using an ontology reuse methodology where terms from the sample reports were mapped to existing biomedical ontologies. It is anticipated that a standardised report based on domain ontology will improve the quality of ultrasound reports and encourage its implementation

Text mining in radiology reports

10.1109/ICDM.2008.150Proceedings - IEEE International Conference on Data Mining, ICDM815-82