2018 Guidelines For Copd Management Algorithms In Java. Houston Hookups!

For Copd In Management Java Guidelines Algorithms 2018

COPD: A review of what's new in the updated GOLD guidelines

Shared System of Care (COPD/HF) Prototype Session 3 - ppt download

7 Dec COPD Treatment: GOLD Guidelines. Long-acting bronchodilators. Almost all patients with COPD who experience more than occasional dyspnea should be prescribed long acting bronchodilator therapy. This could be a long-acting beta agonist (LABA), a long acting muscarinic antagonist (LAMA). 13 Jul to the timely diagnosis of HIV and STIs, initiation of appropriate treatment, and reduced onward disease transmission, repeat screening is consisting of individuals meeting the inclusion criteria who attended study clinics in the 12 months prior to intervention Diabetes Technol Ther ;5(6) 12 Oct Image of the cover of the VA/DoD Clinical Practice Guideline Management of Outpatient Chronic The guideline describes the critical decision points in the Management of Chronic Obstructive Pulmonary Disease (COPD) and provides clear and Algorithm A: Management of COPD in Primary Care.

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The published works in this domain reveal the existence of hundreds of healthcare systems that depend entirely on context aware models [ 37 ]. The Bayesian network was strongly highlighted in literature [ 12 ]. The scenario that can design this context aware application is represented by ontology. Gain Ratio Attribute Eval:

Please note that many of the page functionalities won't work as expected without javascript enabled. Volume 17, Issue 7. No citations found yet 0. Create a SciFeed alert for new publications With following keywords context-aware applications. By following authors Hamid Mcheick. One email with all search results. One email for each search. Open Access This article is freely available re-usable Sensors17 7; doi: In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness.

However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain.

Such algorithm predicts exacerbations with high accuracy read more adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment.

In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms discretization, selection, dependency, and the ordering of the relevant attributes constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model.

Introduction People are currently surrounded by technology increasing their quality of life and facilitating their daily activities. However, there are situations where technology is either difficult to handle or people lack of knowledge about how to 2018 Guidelines For Copd Management Algorithms In Java it. Context-aware intelligent systems try to simplify the interaction between technology and people, by predicting and adapting to their needs.

These systems are based on context which is defined as any information used to characterize the situation of an entity. An entity can be a person, a place or an object [ 1 ]. Thus, the context includes both users and environment information. This information is important to define the interaction between users and the technology that surround them based on the context aware application.

One of the most important issues about context-aware systems is the uncertainty of the context and the prediction of relevant attributes. This uncertainty may concern inadequate information such as inexactness, unreliability, and border with ignorance [ 2 ].

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In this paper, we focus on the Bayesian belief network technique to select relevant attributes and use it to predict e exacerbation that suffers from the uncertainty in COPD area. COPD infections are a combination of small airway obstruction and alveolar destruction, phenomena known as chronic bronchitis and emphysema.

Unfortunately, there is no treatment for COPD, or rather there is no cure to reverse the damage done to the airways and lung function, but therapy can slow its progress, reduce complications, improve quality of life [ 3 ] and avoid exacerbation that is the main reason leading to rapidly worsening of health conditions [ 45 ]. Here we will focus on the exacerbation, which does not even have a clear definition because the true relationship between risk factors and the development of exacerbations are not fully understood [ 6 ]; each one has different signs and symptoms [ 7 ], even biomarkers cannot be relied upon to distinguish between COPD cohort at stable state and at exacerbation 8 ].

However, generally, exacerbation defined as involves impaired lung function, acute event, or sudden worsening of COPD symptoms likely to cause death [ 6 ].

Management of COPD: Update

Thus, because the fluctuation source the diversity of exacerbation symptoms, the predicting of frequent exacerbations is needed to plug the uncertainty gap where logical processing If-Else does not work, and to select the relevant symptoms or attributes.

In this context, Bayesian network has proven its efficiency to handle uncertainty in intelligent environments, more particularly those involving medical applications [ 8 ]. Bayesian network has a great reputation; it is used in many sensitive applications, like detecting brain tumors [ 9 ], predicting the risk of article source among patients waiting for heart surgery [ 10 ], and identifying exacerbations of asthma patients [ 11 ].

Similarly, National Aeronautics and Space Administration NASA has used the Bayesian network in their Vista applications to provide guidance on the possibility of failures in space shuttle propulsion systems [ 12 ].

These four algorithms are not used before in the context of COPD based on our review [ 1314 ]. Exacerbation may contribute to dangerous consequences such as premature death [ 3 continue reading, degradation of quality of life [ 12 ], and deterioration of respiratory function [ 15 ].

This situation can last for several days to several weeks [ 16 ], which requires immediate hospitalization [ 17 ]. That is why we desperately need to prevent exacerbation in COPD. Thus far, no treatment has been found to cure, stop, or prevent exacerbation. The existing medication only dilates the bronchi allowing more air into the alveolus [ 18 ]. Therefore, rapid detection of an exacerbation can reduce its effects, facilitate lungs recovery [ 19 ], and avoid their transition to the 2018 Guidelines For Copd Management Algorithms In Java level of COPD 2018 Guidelines For Copd Management Algorithms In Java, increasing morbidity and disability [ 20 ] in the process.

Thus, daily monitoring of COPD by using context aware application is an essential step to prevent the occurrence and the risk of exacerbation. In summary, this article focuses on select the relevant attributes, predict exacerbation, compare different scenarios of selection and prediction algorithms, and create a context aware application that may help both COPD patients and the medical staff.

Recently, there has been growing interest in COPD, but all similar proposals still do not provide an effective solution, as we will discuss in Section 2.

2018 Guidelines For Copd Management Algorithms In Java

Section 3 surveys the common existing techniques and algorithms used in context aware systems. Section read more presents a model and develops computer-aided support, using the Bayesian network based on contextual information, to detect when patient may be prone to exacerbations; it offers a new opportunity for early medical interventions Doctors, Nurses, etc.

2018 Guidelines For Copd Management Algorithms In Java application can avoid unexpected medical visits, reducing the cost of hospitalization, making patients feel more involved and in control of their own illness, alleviating overcrowding in emergency rooms, hospitals or clinics [ 20 ], and prolonging life [ 21 ]. An implementation, validation and results of our model are given in Section 4.

We conclude this article and give future perspectives in Section 5. Remote monitoring of COPD patient is an interest topic, but the published works in this domain lack of the automation automatic data processing [ 2022 ]. Over the past months, we examined similar types of information system in order to propose a new improved solution.

During this review, we distinguished three types of monitoring systems: Telehealth technology aims to create better-informed environment of personalized health care. The first COPD monitoring system was implemented by [ 23 ], this project was using the traditional telephone line to send oxygen saturation SAO2 data and heart rate to specialists.

In a related works, Vontetsianos et al. In this setting, [ 22 ] addressed how patient personally responds to questionnaires on a daily basis by a four-button device called healthBuddy to be evaluated by nurses at the hospital. All these systems do not take the exacerbation problem into consideration, but only focus on COPD generally. The mentioned propositions require a persistent connection to the network and the specialists [ 22 ], making treatments quite expensive as a manual analysis is needed to complete the medical test [ 2324 ].

Moreover, Mclean et al. In this regard, Halpin et al. InVan der Heijden et al. This model was not fully autonomous because the authors have recourse to experts pneumologists to define the dependency between the attributes, which limit the future evolution of the predictive model.

The ambiguity and the presence of unknown and large exacerbation factors induce the bioinformatics systems to select relevant factors or attributes. Recently, Himes et al. However, these studies do not take into account the exacerbation of COPD.

All these combined algorithms discretization, selection, dependency, and the ordering of the relevant attributes constitute an effective prediction model, comparing to effective ones. Build a tree that maximizes the mutual information between each two attributes Figure 2 b,c. This application is a prototype application to validate the integration of the different parts of this research project. Netica TM is considered the most widely used software in the world to develop the Bayesian network [ ].

Furthermore, Raghavan et al. In Amalakuhan et al. In our previous work [ 31 ], we focused on selecting relevant attributes. These attributes are most likely to detect exacerbation in patients with COPD.

We would like to mention that the area under the receiver operating characteristic curve AUC was the metric of evaluation [ 32 ]. The main objective of this research work is to detect relevant attributes and predict exacerbation of COPD patients.

For this reason primarily, we seek to improve the performance 2018 Guidelines For Copd Management Algorithms In Java see more prediction by using the Bayesian network. Thirdly, we selected the relevant attributes based on Bayesian network using Wrapper-BestFirst.

Fourthly, we compared TAN and K2 methods to create the belief network from a learning base instead of relying on pneumologists, as we have seen in [ 20 ]. Then, we here the relevant attributes based on GainRation formula, and we demonstrated the effectiveness of this order to increase the accuracy AUC of prediction by link a new attribute, instead of randomly choosing it.

Finally, all of these methods and algorithms mixed together in perfect harmony to form our prediction model Section 4. The proposed model is a new contribution that can be applied to various types of prediction in different fields.

As case study, a contextual application based on the following technologies: Weka, Netica-j, and NetBeans prove the concept of this model. The ubiquitous computing systems, introduced by Mark Weiser in [ 33 ], are based on the notion of context-awareness making devices more intelligent, able to recognize the surrounding entities and to react to changing circumstances and environments.

This section summarizes the related works for see more the context, predicting the context-relevant attributes, and surveying reasoning methods. Often, it does not make sense to understand the physical and biological variations that happen to your body isolated from their context; occasionally even those basic processes related to the human genomes require consideration environmental and social context to discover the real reasons of mutations [ 34 ].

The use of context has proven its efficiency for all real-time monitoring system [ 35 ] that may provide the proper services to patients and detect the emergency situation when needed [ 36 ]. The published works in this domain reveal the existence of hundreds of healthcare systems that depend entirely on context aware models [ 37 ]. This feature has been applied to a number of hospital projects, such as Bhattacharyya et al.

In a related development, Garcia-Valverde et al.

2018 Guidelines For Copd Management Algorithms In Java

Similarly, Kennedy et al. In this setting, Bayliss et al. Another important work [ 42 ] proposed a medical diagnosis approach to provide tracking services based on contextual information collected by analyzing life habits and bio status to chronic disease patients. The context has become an integral part of the world of bioinformatics, relying upon context can always give you the ability to make the suitable decisions.

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In this study, we are only interested in the medical context, or the contextual factors that affect and guide decision-support system. Hence, in the field of the COPD and according to two statistical surveys [ 4445 ], lung disease symptoms are closely linked to the demographic features of the patients such as age, gender, country, education level, income level, marital status, and occupation.