Bibliografías temáticas / Patient monitoring

Literatura académica sobre el tema "Patient monitoring"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 28 de julio de 2024

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Artículos de revistas sobre el tema "Patient monitoring"

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Palkhede, Nakul S., Sachin D. Mali y Prof Manisha S. Shelar. "IoT Based Patient Monitoring". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 de junio de 2018): 971–75. http://dx.doi.org/10.31142/ijtsrd14216.

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Kumaran M, Senthil, Chittoria R.K, Bibilash B.S., Friji M.T, Mohapatra D.P. y Dineshkumar S. "Tele-Monitoring of Patient in ICU". New Indian Journal of Surgery 8, n.º 3 (2017): 445–47. http://dx.doi.org/10.21088/nijs.0976.4747.8317.28.

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Morton, Anthony. "PATIENT MONITORING". Australian and New Zealand Journal of Surgery 46, n.º 4 (21 de enero de 2008): 304–9. http://dx.doi.org/10.1111/j.1445-2197.1976.tb03238.x.

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Emmett, C. y P. Hutton. "Patient monitoring". British Medical Bulletin 44, n.º 2 (1988): 302–21. http://dx.doi.org/10.1093/oxfordjournals.bmb.a072253.

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Lakshmi, Ms M., Sai Sri Gaddala, Sandhya Rani Konda, Sahithi Gaja y Spoorthi Begari. "Remote Monitoring of Heart Patient with Auto Defibrillator". International Journal of Research Publication and Reviews 5, n.º 5 (2 de mayo de 2024): 1010–19. http://dx.doi.org/10.55248/gengpi.5.0524.1120.

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Sukuvaara, T., A. Heikelä, EMJ Koskp, H. Nieminen y M. Sydänmaa. "Computerized patient monitoring". Acta Anaesthesiologica Scandinavica 37 (diciembre de 1993): 185–89. http://dx.doi.org/10.1111/j.1399-6576.1993.tb03672.x.

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Doss, Marilyn C. y John W. Waterbor. "Monitoring Patient Satisfaction". Oncology Issues 10, n.º 5 (septiembre de 1995): 16–18. http://dx.doi.org/10.1080/10463356.1995.11904559.

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Malone, Mary P. y Elizabeth L. Pollock. "Monitoring Patient Satisfaction". Oncology Issues 10, n.º 5 (septiembre de 1995): 19–21. http://dx.doi.org/10.1080/10463356.1995.11904560.

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Jenner, C. A. y J. A. Wilson. "Continuous patient monitoring". Anaesthesia 56, n.º 6 (junio de 2001): 591–92. http://dx.doi.org/10.1111/j.1365-2044.2001.2094-12.x.

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Chishti, Ahmed y Iain A. Walker. "Patient monitoring techniques". Surgery (Oxford) 37, n.º 8 (agosto de 2019): 450–59. http://dx.doi.org/10.1016/j.mpsur.2019.05.002.

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Tesis sobre el tema "Patient monitoring"

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Syed, Muhammad Yasir Jafri. "IP BASED PATIENT MONITORING SYSTEMS". Thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-19797.

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Jasti, Madhu Narasimha Rao. "IoT based remote patient health monitoring system". Kansas State University, 2017. http://hdl.handle.net/2097/38268.

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Master of Science
Department of Computer Science
Daniel A. Andresen
With an improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen an adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive this is particularly true in developing countries. As a result, this project is an attempt to solve a healthcare problem currently society is facing. The main objective of the project was to design a remote healthcare system. It’s comprised of three main parts. The first part being, detection of patient’s vitals using sensors, second for sending data to cloud storage and the last part was providing the detected data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress away from hospital premises. The Internet of Things (IoT) concepts have been widely used to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to the patients. Health monitoring for active and assisted living is one of the paradigms that can use the IoT advantages to improve the patient’s lifestyle. In this project, I have presented an IoT architecture customized for healthcare applications. The aim of the project was to come up with a Remote Health Monitoring System that can be made with locally available sensors with a view to making it affordable if it were to be mass produced. Hence the proposed architecture collects the sensor data through Arduino microcontroller and relays it to the cloud where it is processed and analyzed for remote viewing. Feedback actions based on the analyzed data can be sent back to the doctor or guardian through Email and/or SMS alerts in case of any emergencies.
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ALMEIDA, VITOR PINHEIRO DE. "PATIENT-BUDDY-BUILD: CUSTOMIZED MOBILE MONITORING FOR PATIENTS WITH CHRONIC DISEASES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28691@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Este trabalho consiste do desenvolvimento de uma ferramenta para a geração de aplicativos móveis, que possibilita um monitoramento customizado, para o acompanhamento à distância de pacientes com doenças crônicas. A customização ocorre a partir de parâmetros e descrições formais, tais como: preferências do paciente, tipo da doença crônica, processo de acompanhamento desejado pelo seu médico, medicação prescrita e dados sobre o contexto (o entorno) do paciente, estes últimos obtidos de sensores. Com base nestes dados, o sistema irá determinar quais informações são mais relevantes para serem adquiridas do paciente através de questionários ou de sensores disponíveis no dispositivo móvel. Informações relevantes são informações que melhor ajudam a identificar possíveis alterações no processo de monitoramento de um paciente. Estas informações serão enviadas pelo dispositivo móvel, juntamente com os dados dos sensores, para o médico responsável. O processo de acompanhamento médico e a natureza da doença crônica definir ao o conjunto de informações que serão coletadas. É importante ressaltar que o objetivo não é realizar diagnósticos, mas sim, prover informações atualizadas aos médicos sobre os seu pacientes, possibilitando assim, realizar um acompanhamento preventivo à distância.
This thesis consists of the development of a tool for generating mobile applications that enables a customized form of remote monitoring of patients with chronic diseases. The customization is based on parameters and formal descriptions of patient preferences, the type of chronic disease, monitoring procedure required by the doctor, prescribed medication and information about the context (i.e. environment) of the patient, where the later is to be obtained from sensors. Bases on this data, the system will determine which information are more relevant to be acquired from the patient through questionnaires and sensors embedded or connected to the smart phone. Relevant information are information that best helps to identify possible changes in the monitoring process of a patient. This set of information will be sent by the mobile application to the responsible physician. The medical treatment and the kind of chronic disease will define the set of information to be collected. It should be stressed that the goal is not to support automatic diagnosis, but only to provide means for physicians to obtain updated information about their patients, so as to allow remote monitoring of patients.
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Albaba, Adnan. "MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING : MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-409069.

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In this thesis, model-based analysis approach is considered as a possible solution towards a patient-specific point-of-care device for the purpose of electrocardiogram monitoring. Two novel methods are proposed, tested, and quantitatively evaluated. First, a method for estimating the instantaneous heart rate using the morphologicalfeatures of one electrocardiogram beat at a time is proposed. This work is not aimed at introducing an alternative way for heart rate estimation, but rather illustrates the utility of model-basedelectrocardiogram analysis in online individualized monitoring ofthe heart function. The heart rate estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the electrocardiogram and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic electrocardiogram signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable electrocardiogram and heart monitor with encouraging results. Second, a model-based method for patient-specific detection of deformed electrocardiogram beats is proposed. Five parameters of a patient-specific nonlinear electrocardiogram model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal electrocardiogram beats. The utility of the proposed approach is tested by validating it on annotated arrhythmia data recorded underclinical conditions.
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Brouse, Christopher J. "Algorithms & software for intelligent patient monitoring". Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31437.

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In Canada, more deaths occur after a patient experiences an adverse event in a hospital than from breast cancer, motor vehicle accidents and AIDS combined. Current technology contributes to the problem, by adding greater complexity to the clinician's workload. There is still significant promise that computerized assistance can improve patient safety. A clinical monitoring expert system, incorporating algorithms and expert knowledge, could automatically diagnose problems and provide advice on how best to avoid hazard. This thesis investigates the design and performance of algorithms and software for an intelligent patient monitor, which forms the foundation of a clinical monitoring expert system. An algorithm has been developed for detecting electrocautery noise in the electrocardiogram (ECG) using wavelet analysis. Electrocautery noise can lead an expert system to make incorrect diagnoses. In 15 surgical cases spanning 38.5 hours of ECG data, we achieved a false positive rate of 0.71% and a false negative rate of 0.33%. While existing hardware approaches detect activation of the noise source without any ability to assess its impact on the measured ECG, our software approach detects the presence of noise in the signal itself. Furthermore, the software approach is cheaper and easier to implement in a clinical environment than existing hardware approaches. A software framework, called iAssist, has been developed for intelligent patient monitoring. The framework is extensible, flexible, scalable, and interoperable. It supports plugins to perform data acquisition, signal processing, graphical display, data storage, and output to external devices. iAssist currently incorporates the electrocautery noise detection algorithm as a plugin for artifact rejection, as well as two plugins to detect change point events in physiological trends. In 38 surgical cases, iAssist detected 868 events, of which clinicians rated more than 50% as clinically significant and less than 7% as artifacts. Clinicians found iAssist intuitive and easy to use.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Best, Nicola Grace. "Dynamic models for post-transplant patient monitoring". Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321024.

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Otine, Charles. "HIV Patient Monitoring Framework Through Knowledge Engineering". Doctoral thesis, Blekinge Tekniska Högskola [bth.se], School of Planning and Media Design, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00540.

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Uganda has registered more than a million deaths since the HIV virus was first offi¬cially reported in the country over 3 decades ago. The governments in partnership with different groups have implemented different programmes to address the epidemic. The support from different donors and reduction in prices of treatment resulted in the focus on antiretroviral therapy access to those affected. Presently only a quarter of the approximately 1 million infected by HIV in Uganda are undergoing antiretroviral therapy. The number of patients pause a challenge in monitoring of therapy given the overall resource needs for health care in the country. Furthermore the numbers on antiretroviral therapy are set to increase in addition to the stringent requirements in tracking and monitoring of each individual patient during therapy. This research aimed at developing a framework for adopting knowledge engineering in information systems for monitoring HIV/AIDS patients. An open source approach was adopted due to the resource constrained context of the study to ensure a cost effec¬tive and sustainable solution. The research was motivated by the inconclusive literature on open source dimensional models for data warehouses and data mining for monitor¬ing antiretroviral therapy. The first phase of the research involved a situational analysis of HIV in health care and different health care information systems in the country. An analysis of the strengths, weaknesses and opportunities of the health care system to adopt knowledge bases was done. It proposed a dimensional model for implementing a data warehouse focused on monitoring HIV patients. The second phase involved the development of a knowledge base inform of an open source data warehouse, its simulation and testing. The study involved interdisciplinary collaboration between different stakeholders in the research domain and adopted a participatory action research methodology. This involved identification of the most appropriate technologies to foster this collabora¬tion. Analysis was done of how stakeholders can take ownership of basic HIV health information system architecture as their expertise grow in managing the systems and make changes to reflect even better results out of system functionality. Data mining simulations was done on the data warehouse out of which two machine learning algorithms (regression and classification) were developed and tested using data from the data warehouse. The algorithms were used to predict patient viral load from CD4 count test figures and to classify cases of treatment failure with 83% accu¬racy. The research additionally presents an open source dimensional model for moni¬toring antiretroviral therapy and the status of information systems in health care. An architecture showing the integration of different knowledge engineering components in the study including the data warehouse, the data mining platform and user interac-tion is presented.
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Stanford, Maura Marisela. "Reducing Patient Falls and Decreasing Patient Safety Attendant Utilization With CareView Communication Technology". ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7081.

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Attention to quality outcomes, cost reduction, value-based purchasing, and improved initiatives for hospital-acquired conditions (HACs), as well as labor utilization savings, provide a foundation for hospitals to achieve safety and quality outcomes. Falls resulting in fractures, joint dislocation, or other physical injuries are considered HACs and can produce unexpected consequences such as an increase in costs related to an increased length of stay. The CareView Communications system, a fall prevention management program that uses video surveillance technology, offers a strategy to prevent patient falls by customizing patient rounding, conducting fall risk assessments, and generating reports. Guided by Donabedian's framework, this project evaluated the effectiveness of implementation of CareView video monitoring to decrease falls, avoid falls with injury, and reduce use of the patient safety technician on the hospital's telemetry and neurological unit. One year of preimplementation fall data were compared to 1 year of postimplementation data to measure the video monitoring (VM) system effectiveness in fall reduction. Although there was a reduction in the number of falls on the neurological (4.08 to 3.24/1,000 patient days) and the telemetry (2.92 to 1.96.1,000 patient days) units, the results were not statistically significant. The results of this project could contribute to positive social change by helping to determine the effectiveness of the CareView system in reducing falls and identifying strategies for implementing the use of the VM system to reduce patient falls and enhance patient safety.
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Liu, Y. "Wireless remote patient monitoring on general hospital wards". Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/17508/.

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A novel approach which has potential to improve quality of patient care on general hospital wards is proposed. Patient care is a labour-intensive task that requires high input of human resources. A Remote Patient Monitoring (RPM) system is proposed which can go some way towards improving patient monitoring on general hospital wards. In this system vital signs are gathered from patients and sent to a control unit for centralized monitoring. The RPM system can complement the role of nurses in monitoring patients’ vital signs. They will be able to focus on holistic needs of patients thereby providing better personal care. Wireless network technologies, ZigBee and Wi-Fi, are utilized for transmission of vital signs in the proposed RPM system. They provide flexibility and mobility to patients. A prototype system for RPM is designed and simulated. The results illustrated the capability, suitability and limitation of the chosen technology.
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Watman, Geoffrey P. "Pharmacist monitoring of patient health in the community". Thesis, Aston University, 1996. http://publications.aston.ac.uk/10935/.

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This research has explored the potential role of the community pharmacist in health promotion in the pharmacy, and at general medical practices. The feasibility of monitoring patients' health status in the community was evaluated by intervention to assess and alter cardiovascular risk factors. 68, hypertensive patients, monitored at one surgery, had a change in mean systolic blood pressure from 158.28 to 146.55 mmHg, a reduction of 7.4%, and a change in mean diastolic bood pressure from 90.91 to 84.85 mmHg, a reduction of 6.7%. 120 patients, from a cohort of 449 at the major practice, with an initial serum total cholesterol of 6.0+mmol/L, experienced a change in mean value from 6.79 to 6.05 mmol/L, equivalent to a reduction of 10.9%. 86% of this patient cohort showed a decrease in cholesterol concentration. Patients, placed in a high risk category according to their coronary rank score, assessed at the first health screening, showed a consistent and significant improvement in coronary score throughout the study period of two years. High risk and intermediate risk patients showed improvements in coronary score of 52% and 14% respectively. Patients in the low risk group maintained their good coronary score. In some cases, a patient's improvement was effected in liaison with the GP, after a change or addition of medication and/or dosage. Pharmacist intervention consisted of advice on diet and lifestyle and adherence to medication regimes. It was concluded that a pharmacist can facilitate a health screening programme in the primary care setting, and provide enhanced continuity of care for the patient.
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Libros sobre el tema "Patient monitoring"

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McWilliams, Andrew. Patient monitoring devices. Norwalk, CT: Business Communications Co., 2003.

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Jevon, Philip, Beverley Ewens y Jagtar Singh Pooni, eds. Monitoring the Critically III Patient. West Sussex, UK: John Wiley & Sons, Ltd,., 2012. http://dx.doi.org/10.1002/9781118702932.

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Inc, Biomedical Business International, ed. Patient monitoring disposables and equipment. 3a ed. Tustin, CA, U.S.A. (17722 Irvine Blvd., Tustin 92680): Biomedical Business International, 1987.

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Steinberg, Jonathan S. y Suneet Mittal. Remote patient monitoring in cardiology. New York, NY: Demos, 2012.

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Beverley, Ewens, ed. Monitoring the critically ill patient. 3a ed. Chichester, West Sussex: Wiley-Blackwell, 2012.

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Philip, Jevon y Ewens Beverley, eds. Monitoring the critically ill patient. Oxford: Blackwell Science, 2002.

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Rainbow, Carole. Monitoring the critically ill patient: Patient problems and nursing care. Oxford: Heinemann Nursing, 1989.

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National Cholesterol Education Program (U.S.) y Working Group on Management of Patients with Hypertension and High Blood Cholesterol, eds. Working group report on management of patients with hypertension and high blood cholesterol. Bethesda, Md.?]: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute, 1990.

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Dr, Gibson Peter, ed. Monitoring asthma. Boca Raton: Taylor & Francis, 2005.

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Garry, John W., ed. Monitoring glycaemic control in the diabetic patient. London: Harcourt Health Communications, 2001.

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Capítulos de libros sobre el tema "Patient monitoring"

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Grable, Benjamin y Theresa A. Gelzinis. "Patient Monitoring". En Basic Clinical Anesthesia, 69–88. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1737-2_6.

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Lupu, Robert G., Andrei Stan y Florina Ungureanu. "Patient Monitoring: Wearable Device for Patient Monitoring". En Lecture Notes in Electrical Engineering, 659–68. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2311-7_56.

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Gardner, Reed M., Terry P. Clemmer, R. Scott Evans y Roger G. Mark. "Patient Monitoring Systems". En Biomedical Informatics, 561–91. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4474-8_19.

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Gardner, Reed M. y M. Michael Shabot. "Patient-Monitoring Systems". En Medical Informatics, 443–84. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-0-387-21721-5_13.

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Rott, Hannelore y Theodor Koschinsky. "Patient self-monitoring". En Point-of-Care Testing, 319–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-54497-6_33.

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Gardner, Reed M. y M. Michael Shabot. "Patient-Monitoring Systems". En Health Informatics, 585–625. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-36278-9_17.

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Anand, Darpan, Gurpreet Singh y Vishan Kumar Gupta. "Remote Patient Monitoring". En Handbook on Augmenting Telehealth Services, 213–32. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003346289-13.

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Baura, Gail. "Patient Monitoring Devices". En Biosystems Approach to Industrial Patient Monitoring and Diagnostic Devices, A, 53–79. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01625-7_3.

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Herasevich, Vitaly, Brian W. Pickering, Terry P. Clemmer y Roger G. Mark. "Patient Monitoring Systems". En Biomedical Informatics, 693–732. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58721-5_21.

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Van de Velde, Rudi. "Patient Monitoring Systems". En Hospital Information Systems — The Next Generation, 267–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77617-5_25.

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Actas de conferencias sobre el tema "Patient monitoring"

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Lee, Sunghoon Ivan, Hassan Ghasemzadeh, Bobak Mortazavi, Mars Lan, Nabil Alshurafa, Michael Ong y Majid Sarrafzadeh. "Remote patient monitoring". En the 4th Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2534088.2534108.

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Diwakar, Deep, Vipul Vaibhav y Sudipta Majumdar. "Patient Monitoring System". En 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456363.

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Deshmukh, Shubham L., Favin Fernandes, Sakshi Kulkarni, Aniket Patil, Vaishali Jabade y Jyoti Madake. "Patient Monitoring System". En 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). IEEE, 2022. http://dx.doi.org/10.1109/mysurucon55714.2022.9972563.

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Megalingam, Rajesh Kannan, Divya M. Kaimal y Maneesha V. Ramesh. "Efficient Patient Monitoring for Multiple Patients Using WSN". En 2012 International Conference on Advances in Mobile Network, Communication and its Applications (MNCAPPS). IEEE, 2012. http://dx.doi.org/10.1109/mncapps.2012.23.

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Danoff, S. K., W. Tang, S. C. Mathai, M. C. Mccormack, A. Balasubramanian, B. Seth, M. Hill, T. Mao, Z. Hornberger y J. Smith. "Remote Patient Monitoring of ILD Patients Using Oxygen". En American Thoracic Society 2024 International Conference, May 17-22, 2024 - San Diego, CA. American Thoracic Society, 2024. http://dx.doi.org/10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a1793.

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Khan, Dodo, Low Tan Jung, Manzoor Ahmed Hashmani y Moke Kwai Cheong. "Blockchain Enabled Diabetic Patients’ Data Sharing and Real Time Monitoring". En 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120620.

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According to the World Health Organization worldwide diabetes report, the number of diabetic patients has surged from 108 million in the 1980s to 422 million in 2014. According to researchers, the numbers will continue to climb in the next decades. Diabetes is a sickness that requires long-term self-care and close monitoring to be appropriately put under control. As a result, continuous monitoring of blood sugar levels has the potential to save millions of lives. This paper proposes a Blockchain-based platform that connects the patients, healthcare practitioners (HP), and caregivers for a continuous monitoring and care ofdiabetic patients. It lets the patients to securely connected to HP for the purpose of remote patient monitoring (telemedicine), whilst preserving patient data privacy using the blockchain technology. IoT sensors are used to read sugar levels and store these data in a tamper-proof immutable ledger (Hyperledger). This platform provides an End-to-End movement of the patient's data. That is, from the point where it is formed (sensors) to the point it ends up in the HP side. It gives patient a control-and-track function to maintain/track data movement. It provides a unique feature in allowing the patient to keep track of the private data and to pick who they want to share the data with and for how long (and for what reason). The platform is developed in two stages. Initially, the concept is implemented using the Hyperledger Fabric. Then, a Blockchain based on a novel Proof-of-Review (PoR) consensus model is included on to provide efficient performance and scalability in the Hyperledger fabric. Essentially, this proposed platform is to alleviate the pain points in traditional healthcare systems in the scopes of information exchange, data security, and privacy maintenance for real-time diabetic patient monitoring.
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Neves, Sandra, Vera Oliveira y Maria Guarino. "Using Co-Design Methods to Develop a Patient Monitoring System in Hospital Emergency Care to Support Patient Safety". En 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001405.

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Efforts have been made to develop a collaborative model to engage healthcare professionals and patients in healthcare services and resources improvement (Neves et al. 2021) This paper aims to understand how a collaborative model can enhance how design researchers work with healthcare communities in Portugal. Within relation to the development of a patient monitoring system to support patient safety for hospitalised people, this paper reports how design researchers are collaborating with the more traditional healthcare support specialisms in the research team. The design researchers are introducing methods and tools to involve all key stakeholders (i.e., nurses, doctors and patient and public representatives) in the design of the new patient monitoring system, which involves the continuous monitoring of vital signs for early detection of clinical deterioration to ensure patient safety in emergency care at the hospital. Specifically, through the nature of co-design workshops and the use of participative tools, these approaches are intended to better empower patients and healthcare professionals in this co-development process, to allow them to mediate the decision-making process in this context. This paper presents the first phase of this co-development process, highlighting the importance of using a participatory co-design approach to enable healthcare professionals and patients to voice their issues when developing a patient monitoring system.
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Bell, Alexsis, Paul Rogers, Chris Farnell, Brett Sparkman y Scott C. Smith. "Wireless patient monitoring system". En 2014 Health Innovations and POCT. IEEE, 2014. http://dx.doi.org/10.1109/hic.2014.7038896.

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Ghosh, Arijit, Subhajit Aich, Ayan Kumar Ghosh, Pratim Das, Saikat Mahato y Asit Dey. "Patient health monitoring system". En 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI). IEEE, 2016. http://dx.doi.org/10.1109/icicpi.2016.7859685.

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Tan, Li Tung, Raihanah Syamimi Mohammad Shahrizal, Muhammad Naqib'Aqil Mohd Nadzri, Norashikin Yahya y Suhaila Badarol Hisham. "Remote Patient Monitoring System". En 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA). IEEE, 2022. http://dx.doi.org/10.1109/roma55875.2022.9915658.

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Informes sobre el tema "Patient monitoring"

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Pritchard, Nigel D. Tempus Pro Patient Monitoring System. Fort Belvoir, VA: Defense Technical Information Center, abril de 2015. http://dx.doi.org/10.21236/ada615472.

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Cawthra, Jennifer, Nakia Grayson, Ronald Pulivarti, Bronwyn Hodges, Jason Kuruvilla, Kevin Littlefield, Julie Snyder, Sue Wang, Ryan Williams y Kangmin Zheng. Securing Telehealth Remote Patient Monitoring Ecosystem. National Institute of Standards and Technology, febrero de 2022. http://dx.doi.org/10.6028/nist.sp.1800-30.

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Chaparadza, Diana. An Analysis of Patient-Generated Health Data in Assisting Nurses and Physicians to Better Treat Patients with Hypertension. University of Tennessee Health Science Center, noviembre de 2020. http://dx.doi.org/10.21007/chp.hiim.0080.

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Patient Generated Health Data (PGHD is not new but it has gained more attention these past years due to the advent of smart devices, remote monitoring devices and many applications on various smart devices. PGHD reflects medications and treatment, lifestyle choices, and health history. Unlike traditional medical visits, where clinicians collect and manage data within their offices, PGHD is collected by patients throughout the course of their day and provides an insight of how they are responding to treatments or lifestyle choices. Examples include blood glucose monitoring or blood pressure readings using home health equipment, exercise and diet tracking using mobile applications or wearable devices such as the Fitbit or other smart watches.
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Donahue, Katrina, Laura Young, John Buse, Mark Weaver, Maihan Vu, C. Madeline Mitchell, Tamara Blakeney, Kimberlea Grimm, Jennifer Rees y Franklin Niblock. Effect of Glucose Monitoring on Patient and Provider Outcomes in Non-Insulin Treated Diabetes. Patient-Centered Outcomes Research Institute (PCORI), marzo de 2018. http://dx.doi.org/10.25302/3.2018.ce.12114980.

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Sadeghsalehi, Hamidreza, Parinaz Onikzeh, Afshin Heidari, Aida Kazemi, Parisa Najjariasl, Kamran Dalvandi y Hadi Zamanian. Application of smartphone apps in assessment after spine surgeries: a systematic review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, octubre de 2021. http://dx.doi.org/10.37766/inplasy2021.10.0054.

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Review question / Objective: The aim of this systematic review is to investigate applications of smartphone apps in assessment and monitoring of postoperative symptoms and patient functions after spine surgeries. Condition being studied: Some patients with spinal problems, such as Discopathy, need surgery. These patients need frequent follow-up and assessment of symptoms and function after surgery. Currently, the use of mobile applications is a new way to monitor and evaluate patients after spinal surgeries. Information sources: Following databases were searched until 2021-03-16: Pubmed, Scopus, Embase via Embase, Web of Science Core Collection, CINAHL via EBSCO, Cochrane Central Register of Controlled Trials Via Ovid, ACM, Psycinfo.
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Alanazi, Hamad. Exploring Policies and Strategies for the Diffusion of Remote Patient Monitoring (RPM) for the Care of Senior Population. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.7379.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor y Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), diciembre de 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Wan, Huipeng, Hui Xu, Zixuan Wang, Peng Yu y Xiao Liu. The impact of telehealth remote patient monitoring on glycemic control in type 2diabetes: an updated meta-analysis of randomised controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, mayo de 2022. http://dx.doi.org/10.37766/inplasy2022.5.0001.

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Boyes, Allison, Jamie Bryant, Alix Hall y Elise Mansfield. Barriers and enablers for older people at risk of and/or living with cancer to accessing timely cancer screening, diagnosis and treatment. The Sax Institute, julio de 2022. http://dx.doi.org/10.57022/ieoy3254.

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• Older adults have complex and unique needs that can influence how and when cancer is diagnosed, the types of treatment that are offered, how well treatment is tolerated and treatment outcomes. • This Evidence Check review identified 41 studies that specifically addressed barriers and enablers to cancer screening, diagnosis and treatment among adults aged 65 years and older. • Question 1: The main barriers for older people at risk of and/or living with cancer to access and participate in timely cancer screening relate to lack of knowledge, fear of cancer, negative beliefs about the consequences of cancer, and hygiene concerns in completing testing. The main enablers to participation in timely cancer screening include positive/helpful beliefs about screening, social influences that encourage participation and knowledge. • Question 2: The main barriers for older people at risk of and/or living with cancer to access and/or seek timely cancer diagnosis relate to lack of knowledge of the signs and symptoms of cancer that are distinct from existing conditions and ageing, healthcare accessibility difficulties, perceived inadequate clinical response from healthcare providers, and harmful patient beliefs about risk factors and signs of cancer. The main enablers to accessing and/or seeking a timely cancer diagnosis include knowledge of the signs and symptoms of cancer, and support from family and friends that encourage help-seeking for symptoms. • Question 3: The main barriers for older people at risk of and/or living with cancer in accessing and completing cancer treatment include discrimination against patients in the form of ageism, lack of knowledge, patient concern about the adverse effects of treatment, predominantly on their independence, healthcare accessibility difficulties including travel and financial burden, and patients’ caring responsibilities. The main enablers to accessing and completing cancer treatment are social support from peers in a similar situation, family and friends, the influence of healthcare providers, and involving patients in treatment decision making. • Implications. The development of strategies to address the inequity of cancer outcomes in people aged 65 years and older in NSW should consider: ­ Increasing community members’ and patients’ knowledge and awareness by providing written information and decision support tools from a trusted source ­ Reducing travel and financial burden by widely disseminating information about existing support schemes and expanding remote patient monitoring and telehealth ­ Improving social support by promoting peer support, and building the support capacity of family carers ­ Addressing ageism by supporting patients in decision making, and disseminating education initiatives about geriatric oncology to healthcare providers ­ Providing interdisciplinary geriatric oncology care by including a geriatrician as part of multidisciplinary teams and/or expanding geriatric oncology clinics.
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Small Lung Nodule Volume Assessment and Monitoring in Low Dose CT Screening, Clinically Feasible Profile. Chair Artit Jirapatnakul, James Mulshine y Kyle Myers. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), diciembre de 2023. http://dx.doi.org/10.1148/qiba/20231219.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Profile Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) defines the technical methods to be used for evaluating conformance with profile requirements. This includes the steps needed for clinical sites and equipment vendors to be compliant with the Profile. This QIBA Profile (Small Lung Nodule Volume Assessment and Monitoring in Low Dose CT Screening) addresses the accuracy and precision of quantitative CT volumetry as applied to solid lung nodules of 6-10 mm diameter. It places requirements on Acquisition Devices, Technologists, Radiologists and Image Analysis Tools involved in activities including Periodic Equipment Quality Assurance, Subject Selection, Subject Handling, Image Data Acquisition, Image Data Reconstruction, Image Quality Assurance, and Image Analysis. The requirements are focused on achieving sufficient accuracy and avoiding unnecessary variability of the lung nodule volume measurement. Two sets of claims are provided within this Profile. The first claim establishes 95% confidence intervals for volumetric measurement of solid lung nodules for each different millimeter in diameter from 6-10 mm as this is the size range for baseline measurements. The second claim provides guidance on the amount of volumetric change percentage needed for an observer to have 95% confidence that the nodule has exhibited true change. In addition, the second claim also provides guidance on the 95% confidence interval for a volumetric size change measurement, again based on the size of the nodule at two time points. This document is intended to help clinicians reliably measure pulmonary nodule volume as an imaging biomarker, imaging staff generating this biomarker, vendor staff developing related products, purchasers of such products and investigators designing trials with imaging endpoints. Note that this Profile document only states requirements to achieve the claim, not “requirements on standard of care.” Further, meeting the goals of this Profile is secondary to properly caring for the patient. This Profile document includes a conformance test that can be performed with a precision engineered phantom designed to test the fundamental imaging performance characteristics of the CT scanner to be used at a clinical site. The steps to perform the conformance test are described in the Profile and can determine if the site scanner is functioning at a level that would be capable of measuring with accuracy sufficient to meet the requirements of the Profile claim. QIBA Profiles addressing other imaging biomarkers using CT, MRI, PET and Ultrasound can be found at qibawiki.rsna.org.
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