Implementation of the Real-time Algorithms Based on the Symbolic Dynamics of a Coarse-grained Heart Rate Variability in Ubiquitous Health Care Systems

2007 ◽  
pp. 4119-4122
Author(s):  
Sang-Hoon Yi ◽  
Kyung-Tae Park ◽  
Cheol-Seung Yoo ◽  
Gye-Rok Jeon
Keyword(s):  
Health Care ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Symbolic Dynamics ◽  
Health Care Systems ◽  
Coarse Grained ◽  
The Real ◽  
Care Systems

An instrument for the real-time spectral estimation of heart rate variability signals

1993 ◽  
Vol 43 ◽  
pp. 99
Author(s):  
P. Ottonello ◽  
L. Basano ◽  
A. Poggi ◽  
S. Semino ◽  
P. Ubaldi ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Spectral Estimation ◽  
The Real

scholarly journals Algorithm Change Protocols in the Regulation of Adaptive Machine Learning–Based Medical Devices

10.2196/30545 ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. e30545
Author(s):  
Stephen Gilbert ◽  
Matthew Fenech ◽  
Martin Hirsch ◽  
Shubhanan Upadhyay ◽  
Andrea Biasiucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Real Time ◽  
Medical Devices ◽  
Health Care Systems ◽  
World Health ◽  
Health Care Need ◽  
Care Systems ◽  
Health Organization ◽  
Innovative Approaches

One of the greatest strengths of artificial intelligence (AI) and machine learning (ML) approaches in health care is that their performance can be continually improved based on updates from automated learning from data. However, health care ML models are currently essentially regulated under provisions that were developed for an earlier age of slowly updated medical devices—requiring major documentation reshape and revalidation with every major update of the model generated by the ML algorithm. This creates minor problems for models that will be retrained and updated only occasionally, but major problems for models that will learn from data in real time or near real time. Regulators have announced action plans for fundamental changes in regulatory approaches. In this Viewpoint, we examine the current regulatory frameworks and developments in this domain. The status quo and recent developments are reviewed, and we argue that these innovative approaches to health care need matching innovative approaches to regulation and that these approaches will bring benefits for patients. International perspectives from the World Health Organization, and the Food and Drug Administration’s proposed approach, based around oversight of tool developers’ quality management systems and defined algorithm change protocols, offer a much-needed paradigm shift, and strive for a balanced approach to enabling rapid improvements in health care through AI innovation while simultaneously ensuring patient safety. The draft European Union (EU) regulatory framework indicates similar approaches, but no detail has yet been provided on how algorithm change protocols will be implemented in the EU. We argue that detail must be provided, and we describe how this could be done in a manner that would allow the full benefits of AI/ML-based innovation for EU patients and health care systems to be realized.

scholarly journals Real-time cloud system for managing blood units and convalescent plasma for COVID-19 patients

2021 ◽  
Vol 11 (4) ◽  
pp. 3593
Author(s):  
Dhuha Basheer Abdulla ◽  
Mohammed Dherar Younus
Keyword(s):  
Health Care ◽  
Real Time ◽  
Health Care Systems ◽  
Patient Death ◽  
Real Time System ◽  
Blood Unit ◽  
Lower Priority ◽  
Unit Management ◽  
Blood Banks ◽  
Care Systems

<span>In health care systems, blood management services are essential to saving lives. In such systems, when a unit of blood is required, if the system is not able to provide it on time, sometimes this may lead to patient death, especially in critical cases. Unfortunately, even if the required blood unit is available within the system, contradictions may occur and the required blood unit may not be allocated to critical cases on time, due to the allocation of these units to lower priority cases or due to the isolated operate of blood banks within these systems. So, to overcome these obstacles, we proposed a real-time system on a cloud, to managing blood units within the whole health care system. This system will allocate blood units depends on the deadline and the severity of the case that needs blood, in addition to the types, quantities, and position of available blood units. Where, this system eliminated the need for human intervention in managing blood units, in addition to offering the ability to easily develop the system to deal with new urgent requirements, which need new methods of managing blood units; as is happening today with the COVID-19 epidemic. This system increases the performance, transparency, reliability, and accuracy of blood unit management operations while reducing the required cost and effort.</span>

scholarly journals A Theoretical Model to Investigate the Influence of Temperature, Reactions of the Population and the Government on the COVID-19 Outbreak in Turkey

2020 ◽  
pp. 1-9
Author(s):  
Yahya Öz
Keyword(s):  
Health Care ◽  
Transmission Rate ◽  
Health Care Systems ◽  
Real Data ◽  
Economic Consequences ◽  
Implicit Time ◽  
The Real ◽  
Johns Hopkins University ◽  
Care Systems ◽  
The Government

ABSTRACT Objectives: The ongoing coronavirus disease 2019 (COVID-19) pandemic, which was initially identified in December 2019 in the city of Wuhan in China, poses a major threat to worldwide health care. By August 04, 2020, there were globally 695,848 deaths (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). A total of 5765 of them come from Turkey (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). As a result, various governments and their respective populations have taken strong measures to control the spread of the pandemic. In this study, a model that is by construction able to describe both government actions and individual reactions in addition to the well-known exponential spread is presented. Moreover, the influence of the weather is included. This approach demonstrates a quantitative method to track these dynamic influences. This makes it possible to numerically estimate the influence that various private or state measures that were put into effect to contain the pandemic had at time t. This might serve governments across the world by allowing them to plan their actions based on quantitative data to minimize the social and economic consequences of their containment strategies. Methods: A compartmental model based on SEIR that includes the risk perception of the population by an additional differential equation and uses an implicit time-dependent transmission rate is constructed. Within this model, the transmission rate depends on temperature, population, and government actions, which in turn depend on time. The model was tested using different scenarios, with the different dynamic influences being mathematically switched on and off. In addition, the real data of infected coronavirus cases in Turkey were compared with the results of the model. Results: The mathematical study of the influence of the different parameters is presented through different scenarios. Remarkably, the last scenario is also an example of a theoretical mitigation strategy that shows its maximum in August 2020. In addition, the results of the model are compared with the real data from Turkey using conventional fitting that shows good agreement. Conclusions: Although most countries activated their pandemic plans, significant disruptions in health-care systems occurred. The framework of this model seems to be valid for a numerical analysis of dynamic processes that occur during the COVID-19 outbreak due to weather and human reactions. As a result, the effects of the measures introduced could be better planned in advance by use of this model.

160: Cost-Effectiveness of Medical Management Strategies of Nephrolithiasis in Different Health Care Systems

2004 ◽  
Vol 171 (4S) ◽  
pp. 42-43 ◽  
Author(s):  
Yair Latan ◽  
David M. Wilhelm ◽  
David A. Duchene ◽  
Margaret S. Pearle
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Medical Management ◽  
Health Care Systems ◽  
Management Strategies ◽  
Care Systems
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