In this dissertation is proposed a methodology to identify patient's recovery patterns after cardiovascular surgery based on laboratory tests results. The main purpose is to enhance the understanding of the manifestations of postsurgical complications in patients who underwent cardiovascular surgery. The analysis of patients' recovery process is based on the relationship between plasma calcium, ionized calcium and platelet count over time. Laboratory results from the James A. Haley Veterans' Hospital databases, related to patients admitted to the Surgical Intensive Care Unit (SICU) after cardiac surgery (coronary artery bypass, aortic value replacement and mitral valve replacement), are used. These databases contain information regarding commonly ordered tests such as Complete Blood Count tests (CBC) and Basic Metabolic Panel (BMP) for a large group of patients over time. Physicians usually order these tests as a component of screening, routine evaluation, or serial assessment. These test results, contain a large amount of information used by most physicians during the diagnosis process and patient monitoring. This study creates time series of some components of the aforementioned tests to analyze their behavior during the perioperative and postoperative period. Time series based clusters are developed to determine the similarities among tests results from four different types of patients: patients who had a satisfactory recovery process without any manifestation of complications, patients who experienced complications but survived, viii patients who experienced complications and then died during their recovery and patients who died during the perioperative period. As a conclusion, the time series based clustering techniques were able to identify whether a patient is likely to fully recover from the surgery, but it does not have the power to detect effectively results corresponding to a patient experiencing complications. The development of this methodology provides statistical evidence of the differences among different patterns on patient recovery. It is clear that patients experiencing complications have a steeper drop of test results after surgery, and also a non-stable trend towards normal levels. The appropriate use of the proposed methodology could help to timely anticipate complications in patient condition, improve the comprehensiveness of the assessment of patient condition based on laboratory test results and enhance the utilization of laboratory results databases.

Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. 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. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its 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 and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the 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.

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.

Thesis (M.Eng. (Hons.)) -- University of Western Sydney, 2001. / Bibliography : leaves [185]-195 (v. 1).

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-05-04T17:30:47Z No. of bitstreams: 1 ALDENOR FALCÃO MARTINS – DISSERTAÇÃO (PPGEE) 2015.pdf: 15602466 bytes, checksum: 608173ca67ff68da8ae45b321aa82204 (MD5) / Made available in DSpace on 2018-05-04T17:30:47Z (GMT). No. of bitstreams: 1 ALDENOR FALCÃO MARTINS – DISSERTAÇÃO (PPGEE) 2015.pdf: 15602466 bytes, checksum: 608173ca67ff68da8ae45b321aa82204 (MD5) Previous issue date: 2015-04-24 / Segundo o IBGE em 2013, o Brasil apresentava 13% de sua população composta por pessoas acima de 65 anos, somado a isto, o estilo de vida das sociedades ocidentais tem facilitado o aparecimento de doenças crônicas cada vez mais cedo. A premissa é que tornemos mais eficiente a utilização do nosso sistema de saúde, pois este é um recurso escasso. Uma forma de melhorar esta eficiência é assegurar que os tratamentos prescritos serão devidamente seguidos. Quando o paciente se encontra no hospital uma gama de recursos monitora a saúde do paciente oferecendo acompanhamento seguro na eventualidade de um desvio, alertando e armazenando as informações do paciente no decorrer de suas atividades. Um recurso que ajuda no acompanhamento deste paciente é a monitoração remota do paciente, que possibilita que sensores enviem a informação da condição de saúde do paciente e permitam o acompanhamento do mesmo. Sistemas Físicos Cibernéticos (SFC) são entidades computacionais ligadas em rede que operam entidades no mundo físico de maneira cooperativa. Tais sistemas podem ser utilizados em redes de monitoramento remoto de pacientes com o fim de apresentar e ajustar o tratamento de acordo com as recomendações do médico. Este trabalho propõe um passo na direção da autonomia, que permita uma melhor qualidade de vida ao paciente crônico, permitindo que situações conhecidas e dentro de um regime de segurança previamente determinado pelo médico sejam ajustadas. Este trabalho apresenta uma proposta de um Sistema Físico Cibernético (SFC), que permite que adequações ao tratamento previamente elaboradas sejam colocadas em planos de tratamento por meio de agentes inteligentes e de planejadores SAT e sejam disponibilizadas de acordo com a mudança da condição do paciente, através de uma rede monitoramento do paciente, seguindo padrões estabelecidos para dispositivos médicos utilizados em casa que disponibiliza o tratamento ao paciente. O modelo proposto é indicado para o acompanhamento em casa de doenças crônicas através de um coletor central responsável pela coordenação do acompanhamento do paciente. / According to IBGE in 2013 13% of the population had 60 or more years old. As the national population ages, we have to move towards more efficient use of SUS. A way to improve is the closer followup of patient’s evolution by the healthcare professional. At the hospital the patient has access to a set of equipments and expert knowledge capable to correct the treatment path. From this scenario it is easy to imply the need for a change, the current status quo is unbearable financially and cumbersome for patient and doctor routines. A resource that helps is the remote patient monitoring (RPM) , where sensors provide the latest information about patient’s health status and are able to suggest a course correction on the treatment path. A Cyber-Physical System (CPS) is a network of interacting computational entities with physical inputs and outputs that work together towards a goal. A CPS can be part of a RPM in order to present and adjust the treatment according to the healthcare professional recommendations. This work offers a framework for situations where the medical expert knowledge is complete allowing changes on the treatment path be adjusted with minimum risk. Our proposal to deal with the problem is a CPS based remote patient monitoring network where a model for the system is developed based on Multiagent Agent System (MAS) and automatic planning system based on SAT, allowing safe and minimal course correction on treatment paths already set for a patient. This proposal operates through a central hub element responsible to coordinate the followup of the patient.

Complex patient are becoming more and more of a challenge to the health care system given the amount of care they require and the amount of documentation needed to keep track of their state of health and treatment. Record keeping using the EHR makes this easier but mounting amounts of patient data also means that clinicians are faced with information overload. Information overload has been shown to have deleterious effects on care, with increased safety concerns due to missed information. Patient record summarization has been a promising mitigator for information overload. Subsequently, a lot of research has been dedicated to record summarization since the introduction of EHRs. In this dissertation we examine whether unsupervised inference methods can derive patient problem-oriented summaries, that are robust to different patients. By grounding our experiments with HIV patients we leverage the data of a group of patients that are similar in that they share one common disease (HIV) but also exhibit complex histories of diverse comorbidities. Using a user-centered, iterative design process, we design an interactive, longitudinal patient record summarization tool, that leverages automated inferences about the patient's problems. We find that unsupervised, joint learning of problems using correlated topic models, adapted to handle the multiple data types (structured and unstructured) of the EHR, is successful in identifying the salient problems of complex patients. Utilizing interactive visualization that exposes inference results to users enables them to make sense of a patient's problems over time and to answer questions about a patient more accurately and faster than using the EHR alone.

In-patient deterioration is a global phenomena and timely recognition and action improves outcome. Intensive care facilities are scarce and expensive and therefore patient care must be optimal. A retrospective health record analysis was used for this study. The findings indicated that nursing personnel do not recognize patient deterioration timeuously. However, the implementation of an outreach team and clinical markers training program improved the recognition of patient deterioration in general wards with three hours and 40 minutes. It is recommended to implement a comprehensive hospital program that addresses the basic knowledge and skills of general ward personnel to observe, recognize, assess and intervene to patients with clinical deterioration. Together with an extensive training program, a basic physiological parameters guideline to activate a team of experts to the bedside, such as an Outreach team, assist nursing personnel to recognize and manage those patients timeuously and ensure treatment in an appropriate level of care. / Health Studies / M. A. (Health studies)

A Úlcera por Pressão (UP) é uma lesão na pele e em tecidos subjacentes causada pela prolongada exposição de regiões do corpo à pressão. O surgimento de UPs impacta diretamente na qualidade de vida de pacientes acamados, já que são feridas dolorosas, e levam à um aumento no tempo de internação para que seja feito o seu tratamento. Abordagens que utilizam software para monitorar automaticamente pacientes acamados vem sendo propostas para apoiar a prevenção de UP\'s. Por meio de uma revisão sistemática, pode-se identificar o estado da arte de tais abordagens, que são baseadas principalmente em sensores instalados sobre o colchão para identificar pontos de pressão. Para realização do monitoramento por essas abordagens, há necessidade do contato do equipamento com o corpo do paciente. Por conta disso, questões como conforto e a higienização ou troca do equipamento, quando um novo paciente precisa ser monitorado devem ser levadas em consideração. Neste trabalho, foi desenvolvido um sistema para apoio à prevenção de úlcera por pressão (SAPU) que realiza o monitoramento de movimentações e posição de decúbito de uma maneira alternativa às abordagens existentes. São recuperados dados de posição e imagens de profundidade do sensor de movimentos Kinect, que são utilizados por métodos de estimativa de movimentação e posição de decúbito propostos neste trabalho. Assim, não se faz necessário o contato direto do paciente com o equipamento de monitoramento. Além disso, o sistema provê, aos profissionais da saúde, indicadores de movimentação por regiões do corpo, que é uma informação que não é provida por outras abordagens existentes. Um experimento preliminar foi realizado com três participantes, que foram instruídos a realizar uma série de movimentações e troca de posição para avaliação dos métodos de estimativa da posição de decúbito e movimentação utilizados pelo SAPU. Os resultados, apesar de preliminares, dão indícios da viabilidade de sua aplicação para monitoramento de pacientes acamados. / Pressure ulcer (PU) is a lesion on the skin and underlying tissues caused by prolonged exposure of body regions to pressure. PU directly impacts bedridden patients\' quality of life since they are painful and may lead to a prolonged period of hospital internment in order to be treated. Approaches that use software to automatically monitor bedridden patients have been proposed to support pressure ulcer prevention. Through a systematic review, we identified the state of art of these approaches that are mainly based on sensors installed on a mattress to identify pressure points. Since the patient is in contact with the monitoring equipment, issues like comfort and the equipment hygiene or replacement when a new patient is monitored need to be considered. In this paper, we present a system to support pressure ulcer prevention (SAPU) that automatically monitors the patient\'s movements and decubitus positions in an alternative way. Position data and depth images are obtained from the Kinect motion sensor and used by methods we proposed to estimate movement and decubitus positions without any contact of the monitoring equipment with the patient. The system also provides movements indicators by body regions that is an information not given by other approaches. A preliminary experiment have been carried out with three participants that performed a sequence of movements and assumed different decubitus positions so that we could evaluate the methods to estimate the decubitus positions and movements detection that are used by SAPU. Even though the results are preliminary, they provide evidence that these methods can be applied in order to monitor patient\'s movements and decubitus positions.

A Úlcera por Pressão (UP) é uma lesão na pele e em tecidos subjacentes causada pela prolongada exposição de regiões do corpo à pressão. O surgimento de UPs impacta diretamente na qualidade de vida de pacientes acamados, já que são feridas dolorosas, e levam à um aumento no tempo de internação para que seja feito o seu tratamento. Abordagens que utilizam software para monitorar automaticamente pacientes acamados vem sendo propostas para apoiar a prevenção de UP\'s. Por meio de uma revisão sistemática, pode-se identificar o estado da arte de tais abordagens, que são baseadas principalmente em sensores instalados sobre o colchão para identificar pontos de pressão. Para realização do monitoramento por essas abordagens, há necessidade do contato do equipamento com o corpo do paciente. Por conta disso, questões como conforto e a higienização ou troca do equipamento, quando um novo paciente precisa ser monitorado devem ser levadas em consideração. Neste trabalho, foi desenvolvido um sistema para apoio à prevenção de úlcera por pressão (SAPU) que realiza o monitoramento de movimentações e posição de decúbito de uma maneira alternativa às abordagens existentes. São recuperados dados de posição e imagens de profundidade do sensor de movimentos Kinect, que são utilizados por métodos de estimativa de movimentação e posição de decúbito propostos neste trabalho. Assim, não se faz necessário o contato direto do paciente com o equipamento de monitoramento. Além disso, o sistema provê, aos profissionais da saúde, indicadores de movimentação por regiões do corpo, que é uma informação que não é provida por outras abordagens existentes. Um experimento preliminar foi realizado com três participantes, que foram instruídos a realizar uma série de movimentações e troca de posição para avaliação dos métodos de estimativa da posição de decúbito e movimentação utilizados pelo SAPU. Os resultados, apesar de preliminares, dão indícios da viabilidade de sua aplicação para monitoramento de pacientes acamados. / Pressure ulcer (PU) is a lesion on the skin and underlying tissues caused by prolonged exposure of body regions to pressure. PU directly impacts bedridden patients\' quality of life since they are painful and may lead to a prolonged period of hospital internment in order to be treated. Approaches that use software to automatically monitor bedridden patients have been proposed to support pressure ulcer prevention. Through a systematic review, we identified the state of art of these approaches that are mainly based on sensors installed on a mattress to identify pressure points. Since the patient is in contact with the monitoring equipment, issues like comfort and the equipment hygiene or replacement when a new patient is monitored need to be considered. In this paper, we present a system to support pressure ulcer prevention (SAPU) that automatically monitors the patient\'s movements and decubitus positions in an alternative way. Position data and depth images are obtained from the Kinect motion sensor and used by methods we proposed to estimate movement and decubitus positions without any contact of the monitoring equipment with the patient. The system also provides movements indicators by body regions that is an information not given by other approaches. A preliminary experiment have been carried out with three participants that performed a sequence of movements and assumed different decubitus positions so that we could evaluate the methods to estimate the decubitus positions and movements detection that are used by SAPU. Even though the results are preliminary, they provide evidence that these methods can be applied in order to monitor patient\'s movements and decubitus positions.