Information analysis and validation of intelligent monitoring systems in intensive care units (original) (raw)
Related papers
Intelligent Systems in Patient Monitoring and Therapy Management - A Survey of Research Projects
1993
Although today's advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel. In addition, storage, reduction, retrieval, processing, and presentation of information are significant challenges. These problems are most severe in critical care environments such as intensive care units (ICUs) and operating room (ORs) where many events are life-threatening and thus require immediate attention and the implementation of definitive corrective actions. This article focuses on intelligent monitoring and control (IMC), or the use of artificial intelligence (AI) techniques to alleviate some of the common information management problems encountered in health-care environments. The article presents the findings of a survey of over 30 IMC projects. A major finding of the survey is that although signifi...
Patient Monitoring System in the context of Artificial Intelligence in ICU
Web of science[Bulletin of Environment, Pharmacology and Life Sciences], 2022
In the modern-day world, patients with critical conditions get monitored in the intensive care units in which every condition of the patient is monitored and necessary treatment is taken in a timely. These patients are susceptible to many diseases and that’s why many of their important and damaged organs are taken special care of. To provide such an amount of care to a single patient, much of the staff is required on a single patient for 24 hours. Due to such an amount of care, a lot of useful data is generated which can play an important role to understand many important factors which get ignored usually. To make sense of such large data on paper for a doctor is a very difficult task that can consume a lot of time and still we don’t know the analyzed finding are correct or not. To detect high risks and failure of the organs, machine learning can play an important role to detect such events and actions can be taken place promptly. In this paper, findings from a lot of research papers have been discussed and summarized to give the best possible solution. The goal of this research article is to give useful insights that can improve the already available models. Keywords: Patient Monitoring System, Artificial Intelligence, Intensive Care Unit (ICU)
Intelligent system in patient monitoring and therapy management
International Journal of Clinical Monitoring and Computing, 1994
Although today's advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel. In addition, storage, reduction, retrieval, processing, and presentation of information are significant challenges. These problems are most severe in critical care environments such as intensive care units (ICUs) and operating room (ORs) where many events are life-threatening and thus require immediate attention and the implementation of definitive corrective actions. This article focuses on intelligent monitoring and control (IMC), or the use of artificial intelligence (AI) techniques to alleviate some of the common information management problems encountered in health-care environments. The article presents the findings of a survey of over 30 IMC projects. A major finding of the survey is that although significant advances have been made in introducing AI technology in critical care, successful examples of fielded systems are still few and far between. Widespread acceptance of these systems in critical care environments depends on a number of factors, including fruitful collaborations between clinicians and computer scientists, emphasis on evaluation studies, and easy access to clinical information.
Integration of data, information and knowledge in intelligent patient monitoring
Expert Systems with Applications, 1998
Efficient patient monitoring requires the integration of bedside monitors, database information and the application of artificial intelligence (AI) techniques, in order to obtain correct interpretations and to prescribe appropriate therapies. In this article, the authors present the new architecture of PATRICIA, an intelligent monitoring system designed to advise clinicians in the management of patients in the intensive care unit (ICU). The system's new architecture is based on current trends in the design of hospital health care systems, and allows integration of bedside monitors to front-end computers, and through the data network to a central monitor that controls and manages all the network operations. We have applied the client-server philosophy that takes advantage from information integration, shared resources and equipment networking. This approach results in an efficient and flexible system, and offers several benefits from the clinical point of view, as it serves as a helping tool for clinical decision-making in an ICU environment. ᭧
ArXiv, 2018
Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performe...
Information Architecture for Intelligent Decision Support in Intensive Medicine
Daily, a great amount of data that is gathered in intensive care units, which makes intensive medicine a very attractive field for applying knowledge discovery in databases. Previously unknown knowledge can be extracted from that data in order to create prediction and decision models. The challenge is to perform those tasks in real-time, in order to assist the doctors in the decision making process. Furthermore, the models should be continuously assessed and optimized, if necessary, to maintain a certain accuracy. In this paper we propose an information architecture to support an adjustment to the INTCare system, an intelligent decision support system for intensive medicine. We focus on the automatization of data acquisition avoiding human intervention, describing its steps and some requirements.
Procedia Technology, 2012
Using the information regarding critical events to support decision making in Intensive Care Units would be useful. However it is seldom used in real settings as the information regarding those critical events is difficult to gather and make available in real time. The most usual procedures record only those events that are related to errors. This paper presents a solution to obtain critical events from clinical data. From data collected using an automatic and real-time data acquisition system it is possible to calculate the critical events regarding five variables that are usually monitored in an ICU. These results are presented to the medical and nursing staff in a friendly and intuitive mode. Using a color code our system provides visual warnings related to the evolution of the monitored variables values. Actually, a quick glance allows doctors to get a high level overview of the patient's condition
Knowledge discovery and knowledge validation in intensive care
Artificial Intelligence in Medicine, 2000
Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that support the development of operational protocols. The aim is to ensure high quality standards for the protocol through empirical validation during the development, as well as lower development cost through the use of machine learning and statistical techniques. We demonstrate our approach of integrating expert knowledge with data driven techniques based on our effort to develop an operational protocol for the hemodynamic system.
Pervasive and Intelligent Decision Support in Intensive Medicine – The Complete Picture
Information Technology in Bio- and Medical Informatics, 2014
In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don't make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients' critical events and for evaluating medical scores automatically and in real time.