Autonomous platform for life-critical decision support in the ICU

Part 2: PhD Workshop: Autonomic Network and Service ManagementInternational audienceThe Intensive Care Unit is a complex, data-intensive and critical environment in which the adoption of Information Technology is growing. As physicians become more dependent on the computing technology to support decisions, raise real-time alerts and notifications of patient-specific conditions, this software has strong dependability requirements. The dependability challenges are expressed in terms of availability, reliability, performance, usability and maintenance of the system. Our research focuses on the design and development of a generic autonomous ICU service platform. COSARA is a computer-based platform for infection surveillance and antibiotic management in ICU. During its design, development and evaluation, we identified both technological and human factors that affect robustness. We presented the identified research questions that will be addressed in detail during PhD research

Design and implementation of a secure and user-friendly broker platform supporting the end-to-end provisioning of e-homecare services

We designed a broker platform for e-homecare services using web service technology. The broker allows efficient data communication and guarantees quality requirements such as security, availability and cost-efficiency by dynamic selection of services, minimizing user interactions and simplifying authentication through a single user sign-on. A prototype was implemented, with several e-homecare services (alarm, telemonitoring, audio diary and video-chat). It was evaluated by patients with diabetes and multiple sclerosis. The patients found that the start-up time and overhead imposed by the platform was satisfactory. Having all e-homecare services integrated into a single application, which required only one login, resulted in a high quality of experience for the patients

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

NEOREG : design and implementation of an online neonatal registration system to access, follow and analyse data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is imortant that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Background: Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echostate approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. Methods: This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay < 10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB). Results: The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm. Conclusions: This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echostate network was more easily configured than other time series modeling technologies

Use of web services for computerized medical decision support, including infection control and antibiotic management, in the intensive care unit

The increasing complexity of procedures in the intensive care unit (ICU) requires complex software services, to reduce improper use of antibiotics and inappropriate therapies, and to offer earlier and more accurate detection of infections and antibiotic resistance. We investigated whether web-based software can facilitate the computerization of complex medical processes in the ICU. The COSARA application contains the following modules: Infection overview, Thorax, Microbiology, Antibiotic therapy overview, Admission cause with comorbidity and admission diagnosis, infection linking and registration, and Feedback. After the implementation and test phase, the COSARA software was installed on a physician's off ice PC and then on the bedside PCs of the patients. Initial evaluation indicated that the services had been integrated easily into the daily clinical workflow of the medical staff. The use of a service oriented architecture with web service technology for the development of advanced decision support in the ICU offers several advantages over classical software design approaches