An apparatus and method for monitoring financial securities markets or financial securities to provide information regarding the status of the financial securities markets or securities. The apparatus (10) comprises sensors (12) for measuring the values of a plurality of parameters associated with the system of interest. The measured values are transferred to a processor (15). Processor (15) comprises an interface (18) for transferring a system profile to a transformer (16). The transformer (16) maps and transforms measured values of more than one parameter using a sigmoid function dependent on at least baseline and critical values of the parameters. The transformer also generates a deviation indicator representing the level of performance associated with each of the parameters. The deviation indicators are then analyzed and displayed on a display unit (22) the status of the financial securities or financial securities.

Monitoring device for financial securities

We describe a prototype context aware perioperative information system to capture and interpret data in an operating room of the future. The captured data is used to construct the context of the surgical procedure and detect medically significant events. Such events, and other state information, are used to automatically construct an electronic medical encounter record (EMR). The EMR records and correlates significant medical data and video streams with an inferred higher-level event model of the surgery. Information from sensors such as Radio Frequency Identification (RFID) tags provides basic context information including the presence of medical staff, devices, instruments and medication in the operating room (OR). Patient monitoring systems and sensors such as pulse oximeters and anesthesia machines provide continuous streams of physiological data. These low level data streams are processed to generate higher-level primitive events, such as a nurse entering the OR. A hierarchical knowledge-based event detection system correlates primitive events, patient data and workflow data to infer high-level events, such as the onset of anesthesia. The resulting EMR provides medical staff with a permanent record of the surgery that can be used for subsequent evaluation and training. The system can also be used to detect potentially significant errors. It seeks to automate some of the tasks done by nursing staff today that detracts from their ability to attend to the patient.

/pdf/a-pervasive-computing-system-for-the-operating-room-of-the-2tppv8r9dl.pdf

A pervasive computing system for the operating room of the future

The diversity of factors that take part in the design of intelligent patient monitoring and management systems is presented. Particular attention is given to intelligent monitoring and management interfaces, and to the use of knowledge-based systems in patient monitoring and management. >

Intelligent patient monitoring and management systems: a review

http://www.meduniwien.ac.at/kpa/publications/1994_FSAS_--_Clinical_Monitoring_with_Fuzzy_Automata.pdf

Clinical monitoring with fuzzy automata

Patient monitoring methods comprising the steps of measuring a plurality of medical parameters, transforming each medical parameter to a function indicative of at least normal and critical levels of the parameter, comparing each function to a prescribed sequence of reference values and on the basis of the comparison generating for each parameter a corresponding danger level, selecting one of the danger levels as a vital function status (VFS) indicator, and displaying the VFS indicator. Each parameter is transformed to a sigmoid function in accordance with pre-selected maximum and minimum values and a pre-selected baseline value adapted to the patient being monitored. A maximum value of the danger levels is selected as the VFS indicator. The VFS indicator will be such that any physician can have, without any previous knowledge of the patient, a reasonably good idea as to the condition of the patient.

Monitoring system for producing patient status indicator

The combination and aggregation of separate information sources to generate more 'intelligent' alarms using a knowledge-based approach is described. The approach gas developed as part of the knowledge-based anesthesia decision support system, AES-2, which monitors the patient's physiological state during anesthesia and eventually suggests therapeutic actions. Integration of the alarm system into a conventional information system is discussed. The state variable model used and the modeling of uncertainty are examined. The tools that support knowledge acquisition, design of knowledge-base prototypes. and test of the knowledge base of the AES-2 are described. A simulation example is given. >

A knowledge-based approach to intelligent alarms in anesthesia

An approach to generating 'intelligent alarms' is presented that aggregates many information items, i.e. measured vital signs, recent medications, etc., into state variables that more directly reflect the patient's physiological state. Based on these state variables the described decision support system AES-2 also provides therapy recommendations. The assessment of the state variables and the generation of therapeutic advice follow a knowledge-based approach. Aspects of uncertainty, e.g. a gradual transition between 'normal' and 'below normal', are considered applying a fuzzy set approach. Special emphasis is laid on the ergonomic design of the user interface, which is based on color graphics and finger touch input on the screen. Certain simulation techniques considerably support the design process of AES-2 as is demonstrated with a typical example from cardioanesthesia.

Knowledge-based decision support for patient monitoring in cardioanesthesia

Eine wissensbasierte Entscheidungsunterstutzung zur Patientenuberwachung stellt besondere Anforderungen an die Wissensakquisition, Wissensreprasentation (einschlieslich unscharfer Wissenskomponenten) und an die Validierung. Daruber hinaus mus mit geeigneten Methoden die Mensch-Maschine-Schnittstelle an die Bedurfnisse des Benutzers sowie an seine kognitiven Fahigkeiten und Grenzen angepast werden. Diese Fragestellungen werden exemplarisch an einer Aufgabenstellung aus der Anasthesie erlautert. Mogliche Losungen werden am Beispiel des Systems AES-2 diskutiert. Dieses System wird entwickelt zur Generierung “intelligenter Alarme” und geeigneter Therapie-Hinweise wahrend einer Operation.

Knowledge-Based Decision Support for Monitoring in Anesthesia: Problems, Design and User Interaction

One of the most important tasks of the anaesthetist performing anaesthesia to a patient undergoing cardiac surgery is the evaluation of the patient's hemodynamic state. During cardiac operation when the heart comes to work again after hours of cardioplegia the vital parameters (blood pressure, heart rate, etc.) can reach extreme values and fluctuations. Current alarm facilities are triggered yielding an alarm cascade and often turned off because the permanent noise level in the operating theatre disturbs the surgical team. An intelligent alarm system to support the anaesthetist in this situation has been developed. The system works online, gathering all required data from a general anaesthesia information system. Based on the decision making process of the anaesthetist, the most important vital parameter constellations are evaluated using a fuzzy inference approach, and are presented graphically on the user interface. >

http://ieeexplore.ieee.org/iel2/1124/8016/00343546.pdf

A fuzzy logic approach to intelligent alarms in cardioanesthesia

Knowledge-based decision support systems for use in cardio-anesthesia can provide online support to the anesthesiologist by generating intelligent alarms [13]. However, the acquisition and validation of a consistent knowledge base for this application bears problems related to the transfer of clinical experiences into a rule system. An interactive simulator of the human circulation is presented that supports the process of knowledge acquisition and testing. The simulator can be controlled in realtime by an anesthesiologist during the simulation run thus providing a basis for interdisciplinary discussion of routine as well as critical situations. The output data can be transfered to a knowledge-based system for test purposes. The simulator is currently being used for the development of the Anesthesia Expert Assist System AES-2 [14]. With regard to the special application a model of the heart-function was integrated which enables the simulation of heart insufficiency. Simulation runs under various conditions are presented and discussed. The simulator was implemented on an ATARI ST personal computer.

H. Käsmacher

Papers

A knowledge-based approach to intelligent alarms in anesthesia

Knowledge-based decision support for patient monitoring in cardioanesthesia

Knowledge-Based Decision Support for Monitoring in Anesthesia: Problems, Design and User Interaction

A fuzzy logic approach to intelligent alarms in cardioanesthesia

An interactive computer simulator of the circulation for knowledge acquisition in cardio-anesthesia.