Studies on relations between number of sessions and effect of psychotherapy have usually assumed a constant rate of change across different lengths of therapy, explained by a model called the dose-effect model. This assumption has been challenged by the good-enough level (GEL) model, which makes the prediction that the rate of change will vary as a function of total number of sessions. This study aimed to compare these models. We also assessed the relationship between reliable and clinically significant change (RCSI) and total dose of therapy. Participants were drawn from two datasets in the Swedish primary care (n = 640) and adult psychiatric care (n = 249). The participants made session-wise ratings on the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM). Multilevel analyses indicated a better fit using the GEL-model, with some reservations concerning RCSI and patterns of change. The results may indicate a general lawful relationship that may have implications for future research, as well as psychotherapy practice and policy making.

Man has a limited ability to accurately and continuously analyse large amounts of data. Observers are typically required to monitor displays over extended periods and to execute overt detection responses to the appearance of low probability critical signals. The signals are usually clearly perceivable when observers are alerted to them, but they can be missed in the operating environment. The challenge is to develop a computer application that will accumulate information on a variable, or several variables, over time and identify when the trend in observations has changed. In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring systems, expert systems and many other computer aided protocols. The expert systems have the potential to improve clinician performance by accurately executing repetitive tasks, to which humans are ill-suited. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. The decision support tools capable of detecting pathological events can enhance the anaesthetist’s performance by providing alternative diagnostic information. The main goal of this research was to develop a clinically useful diagnostic alarm system using two different techniques for monitoring a pathological event during anaesthesia. Several techniques including fuzzy logic, artificial neural networks, control and monitoring techniques were explored. Firstly, an industrial monitoring system called Supervisory Control and Data Acquisition (SCADA) software is used and implemented in the form of a prototype system called SCADA monitoring system (SMS). The output of the system in detecting hypovolaemia was classified into three levels; mild, moderate and severe using SCADA’s InTouch software. In addition, a new GUI display was developed for direct interaction with the anaesthetists. Secondly, a fuzzy logic monitoring system (FLMS) was developed using the fuzzy logic technique. New diagnostic rules and membership functions (MF) were developed using MATLAB. In addition, fuzzy inference system FIS, adaptive neuro fuzzy inference system ANFIS and clustering techniques were explored for developing the FLMS’s diagnostic modules. The raw physiological patient data acquired from an S/5 monitor were converted to a readable format using the DOMonitor application. The data was filtered, preprocessed, and analysed for detecting anaesthesia related events like hypovolaemia. The accuracy of diagnoses generated by SMS and FLMS was validated by comparing their diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s, SMS’s, and FLMS’s diagnoses. In offline analysis both systems were tested with data from 15 patients. The SMS and FLMS achieved an overall agreement level of 87 and 88 percent respectively. It implies substantial level of agreement between SMS or FLMS and the anaesthetists. These diagnostic alarm systems (SMS and FLMS) have shown that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in providing decision support to anaesthetists.

Man has a limited ability to accurately and continuously analyse large amounts of data. Observers are typically required to monitor displays over extended periods and to execute overt detection responses to the appearance of low probability critical signals. The signals are usually clearly perceivable when observers are alerted to them, but they can be missed in the operating environment. The challenge is to develop a computer application that will accumulate information on a variable, or several variables, over time and identify when the trend in observations has changed. In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring systems, expert systems and many other computer aided protocols. The expert systems have the potential to improve clinician performance by accurately executing repetitive tasks, to which humans are ill-suited. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. The decision support tools capable of detecting pathological events can enhance the anaesthetist’s performance by providing alternative diagnostic information. The main goal of this research was to develop a clinically useful diagnostic alarm system using two different techniques for monitoring a pathological event during anaesthesia. Several techniques including fuzzy logic, artificial neural networks, control and monitoring techniques were explored. Firstly, an industrial monitoring system called Supervisory Control and Data Acquisition (SCADA) software is used and implemented in the form of a prototype system called SCADA monitoring system (SMS). The output of the system in detecting hypovolaemia was classified into three levels; mild, moderate and severe using SCADA’s InTouch software. In addition, a new GUI display was developed for direct interaction with the anaesthetists. Secondly, a fuzzy logic monitoring system (FLMS) was developed using the fuzzy logic technique. New diagnostic rules and membership functions (MF) were developed using MATLAB. In addition, fuzzy inference system FIS, adaptive neuro fuzzy inference system ANFIS and clustering techniques were explored for developing the FLMS’s diagnostic modules. The raw physiological patient data acquired from an S/5 monitor were converted to a readable format using the DOMonitor application. The data was filtered, preprocessed, and analysed for detecting anaesthesia related events like hypovolaemia. The accuracy of diagnoses generated by SMS and FLMS was validated by comparing their diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s, SMS’s, and FLMS’s diagnoses. In offline analysis both systems were tested with data from 15 patients. The SMS and FLMS achieved an overall agreement level of 87 and 88 percent respectively. It implies substantial level of agreement between SMS or FLMS and the anaesthetists. These diagnostic alarm systems (SMS and FLMS) have shown that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in providing decision support to anaesthetists.

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The main goal of the master thesis is design and implementation of information system for monitoring purposes. The implemented monitoring system will be able to automatically monitor devices of a certain company. This goal is preceded by strategic analysis using PEST and 7S methods for examination of current state of the company. Another part is the analysis of the efficiency of service processes using the HOS method together with the selection of a suitable monitoring system. An enhancement of service processes is designed by the Lewin model. Finally, the selected information system for monitoring was designed, implemented and tested. A deployment of the system should provide simplification of devices supervision, to save costs as well as to speed-up the response-time to unexpected situations.

Complete overview of energy use in a residential building is depends on many different factors. When analyzing proper and effective ways for energy reducing/conserving systems, often times only technological solutions for households appliances are considered. Human behavior has been shown to be an important factor affecting the overall energy use in the household. Many aspects of energy use are directly connected to user behavior and are affected by how the user utilizes available systems. This paper focuses on describing the mean influencing causes of human environmental psychology based on study on a Swedish suburb community, called Fårdala. User behaviors and actions affecting residential energy use are analyzed and presented in form of eleven (11) abstract triggers to households energy use. Finally an energy monitoring system based on the findings are purposed. What is found from study on human psychology, shows that human behavior is mainly controlled by three (3) key categories of behavior. Conscious/voluntary behavior, Socio-environmental/cultural based behavior and Systemic/learned behavior. Out of the three, while the last one poses as most influential on behavior related energy use, it is also the hardest to affect and change. To effectively counteract the negative effects of user behavior on residential energy use, energy saving devices should react more accordingly to the users and offer engagement. Such a system is an energy monitoring device, which allows for a “double-sided” communication with the user. The user is presented with relevant information about real-time energy use of all of the systems and is able to make changes on the fly. The system should also be able to learn and apply energy saving actions based on user behavior.

No description available.