Statistical Methods in Intensive Care Online Monitoring

Intelligent alarm systems are needed for adequate bedside decision support in critical care. Clinical information systems acquire physiological variables online in short time intervals. To identify complications as well as therapeutic effects procedures for rapid classiffication of the current state of the patient have to be developed. Detection of characteristic patterns in the data can be accomplished by statistical time series analysis. In view of the high dimension of the data statistical methods for dimension reduction should be used in advance. We discuss the potential of statistical techniques for online monitoring

Medical Knowledge Discovery Systems: Data Abstraction And Performance Measurement

Knowledge discovery systems can be traced back to their origin, artificial intelligence and expert systems, but use the modern technique of data mining for the knowledge discovery process. To that end, the technical community views data mining as one step in the knowledge discovery process, while the non-technical community seems to view it as encompassing all of the steps to knowledge discovery. In this exploratory study, we look at medical knowledge discovery systems (MKDSs) by first looking at three examples of expert systems to generate medical knowledge. We then expand on the use of data abstraction as a pre-processing step in the comprehensive task of medical knowledge discovery. Next, we look at how performance of a medical knowledge discovery system is measured. Finally, the conclusions point to a bright future for MKDSs, but an area that needs extensive development to reach its full potential

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)\u27s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model. METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information. RESULTS: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871). CONCLUSIONS: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction

Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes.</p> <p>Methods</p> <p>We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity.</p> <p>Results</p> <p>We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines.</p> <p>Conclusion</p> <p>Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from practice-based knowledge. It can also be used as an evaluation tool for comparing a physician's therapeutic decisions with those recommended by a given set of clinical guidelines. The example we described showed that physician practice was in some ways ahead of the guideline.</p

Learning rules from multisource data for cardiac monitoring

International audienceThis paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure measures, describe cardiac behaviours from different viewpoints. To learn interpretable rules, we use an Inductive Logic Programming (ILP) method. We develop an original strategy to cope with the dimensionality issues caused by using this ILP technique on a rich multisource language. The results show that our method greatly improves the feasibility and the efficiency of the process while staying accurate. They also confirm the benefits of using multiple sources to improve the diagnosis of cardiac arrhythmias

Pattern Recognition in Intensive Care Online Monitoring

Clinical information systems can record numerous variables describing the patient’s state at high sampling frequencies. Intelligent alarm systems and suitable bedsidedecision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of important patterns of change such as level shifts and trends in the data. We present approaches for automated pattern detection in online-monitoring data. Several methods based on curve fitting and statistical time series analysis are described. Median filtering can be used as a preliminary step to reduce the noise and to remove clinically irrelevant short term fluctuations. Our special focus is the potential of these methods for online-monitoring in intensive care. The strengths and weaknesses of the methods are discussed in this special context. The best approach may well be a suitable combination of the methods for achieving reliable results. Further investigations are needed to further improve the methods and their performance should be compared extensively in simulation studies and applications to real data

Sistemas de apoio à decisão na medicina intensiva baseados na descoberta de conhecimento em base de dados

Dissertação de mestrado em Engenharia e Gestão de Sistemas de InformaçãoA dissertação do Mestrado intitulada “Sistemas de Apoio à Decisão para a Medicina Intensiva baseados na Descoberta de Conhecimento em Base de Dados” insere-se na área dos sistemas de informação inteligentes para a medicina intensiva e pretende demonstrar o estado da arte, apresentar a arquitectura de sistemas de informação e todo o trabalho desenvolvido com o objectivo de criar um Sistema de Apoio à Decisão Inteligente (SADI) para a Medicina Intensiva. O aparecimento da medicina intensiva veio possibilitar a recuperação de doentes em fase terminal ou em estado de falência orgânica. Esta recuperação depende, em muito das decisões que são tomadas nas Unidades de Cuidados Intensivos, pois estas podem influenciar mais o outcome de um doente do que qualquer intervenção inovadora que possa ser realizada. Nesse sentido, é importante que todas as informações necessárias para a decisão estejam num formato electrónico. Esta dissertação está enquadrada no projecto de Investigação INTCare e tem como base para a construção do SADI o trabalho desenvolvido no passado. De modo a obter toda informação necessária, definida anteriormente, foi essencial a reformulação da arquitectura de Sistemas de Informação de modo a que esta possibilitasse a recolha e armazenamento dos dados em tempo real e em modo online. A necessidade de encontrar uma solução para a recolha dos sinais vitais e para armazenamento de alguns dos dados que eram registados de forma manuscrita como os da Folha de Enfermagem fez com que fossem analisados outros sistemas semelhantes. Foram ainda definidos alguns dos factores importantes para a decisão e apresentado o modelo de informação para esse sistema. Neste documento é possível averiguar o progresso que se tem verificado na medicina intensiva relativamente aos SADI e à forma como os dados são recolhidos. Um dos sistemas é o INTCare que, através dos seus vários agentes, permite uma monitorização e aquisição dos dados em tempo real, dados esse que, através de técnicas de Inteligência Artificial são transformados em conhecimento, permitindo assim, a construção de modelos de previsão e decisão que serão integrados num Sistemas de Apoio à Decisão Inteligente.Dissertação realizada no âmbito de um projeto de Investigação financiado pela FCT: INTCar

Os impactos do sistema PACS na reorganização de serviços de saúde hospitalar : o caso do Centro Hospitalar Médio Ave (CHMA)

Dissertação de mestrado em Gestão de Unidades de SaúdeUma das principais particularidades do setor da saúde é a necessidade do recurso a novas tecnologias, domínio em que os serviços de radiologia se destacam. A passagem da película para a comunicação interna das imagens em formato digital foi um dos avanços de maior relevo sentidos nos últimos tempos nas unidades de saúde. A presente dissertação de mestrado em Gestão de Unidades de Saúde tem como principal objetivo determinar os impactos do sistema PACS, Pictures Archiving and Communication System (em português, Sistema de Arquivo e Comunicação de Imagens), na reorganização dos serviços hospitalares no Centro Hospitalar Médio Ave (CHMA). Para tal, foi realizado um estudo de caso, no qual se efetuou uma análise qualitativa de oito entrevistas realizadas a colaboradores do CHMA. O painel de entrevistados foi constituído por elementos-chave do projeto de implementação do PACS e por outros elementos vitais para esta investigação. Os dados obtidos da análise das entrevistas revelaram, entre outros aspetos, que a implementação do PACS alterou a rotina de alguns serviços, o que por sua vez se traduziu em impactos para o paciente, o diagnóstico e a instituição. Apesar disso, verifica-se que, para um melhor funcionamento do sistema "PACS", deveriam ser implementadas algumas melhorias, nomeadamente ao nível da formação complementar (reciclagem). Por outro lado, de acordo com a análise e reflexão levadas a cabo no presente estudo, o PACS ainda tem um longo percurso a percorrer no setor da saúde em Portugal. Neste sentido, fazem-se aqui algumas recomendações para o desenvolvimento de uma política de partilha de recursos tecnológicos (PACS) como forma de tornar as despesas de saúde menos avultadas. Desta forma, crê-se que os resultados desta investigação poderão contribuir para a promoção da implementação de sistemas PACS, que são cada vez mais abrangentes na sua capacidade de integração com outros sistemas. Em suma, a implementação de um sistema PACS bem planeada pode simplificar o fluxo de trabalho em todo o hospital e contribuir para um Serviço Nacional de Saúde mais eficiente.One of the main peculiarities of the health sector is the need to use new technologies, an area in which radiology services stand out. The x-ray film passing for internal communication to digital format images was one of the most prominent advances seen in health units in recent years. This thesis in Health Unit Management has as main objective to determine the impacts of the PACS (Pictures Archiving and Communication System) in the reorganization of the hospital services at the Médio Ave Hospital Center (CHMA). For this effect, a case study was carried out in which eight interviews with employees from the CHMA were analyzed. The interviewee panel was composed of key elements of the PACS implementation project and other essential elements to this investigation. The data obtained from the interview analysis revealed, among other aspects, that the implementation of the PACS changed the routine of some services, which, in turn, has had impact on the patient, the diagnosis and the institution. Nevertheless, it appears that, for a better functioning of the "PACS", some improvements should be implemented, particularly in terms of additional training (recycling). On the other hand, according to the analysis and reflection carried out in the present study, the PACS have a long way to go in the health sector in Portugal. In this sense, this thesis presents some recommendations for the development of a policy of technological resource (PACS) sharing as a way of reducing health spending. Thus, it is believed that these results may contribute to the promotion of implementing the PACS systems, which are increasingly wide-ranging in their ability to integrate with other systems. In short, the implementation of a well-planned PACS system can simplify the workflow throughout the hospital and contribute to a more efficient National Health Service