ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2008 (ending).

/pdf/esc-guidelines-for-the-diagnosis-and-treatment-of-acute-and-1bgpp8m4g3.pdf

Esc guidelines for the diagnosis and treatment of acute and chronic heart failure 2008 (ending)

Artificial Intelligence in Precision Cardiovascular Medicine

Despite the advancement in medicine, management of heart failure (HF), which usually presents as a disease syndrome, has been a challenge to healthcare providers. This is reflected by the relatively higher rate of readmissions along with increased mortality and morbidity associated with HF. In this review article, we first provide a general overview of types of HF pathogenesis and diagnostic features of HF including the crucial role of exercise in determining the severity of heart failure, the efficacy of therapeutic strategies and the morbidity/mortality of HF. We then discuss the quality control measures to prevent the growing readmission rates for HF. We also attempt to elucidate published and ongoing clinical trials for HF in an effort to evaluate the standard and novel therapeutic approaches, including stem cell and gene therapies, to reduce the morbidity and mortality. Finally, we discuss the appropriate utilization/documentation and medical coding based on the severity of the HF alone and with minor and major co-morbidities. We consider that this review provides an extensive overview of the HF in terms of disease pathophysiology, management and documentation for the general readers, as well as for the clinicians/physicians/hospitalists.

https://www.mdpi.com/2077-0383/5/7/62/pdf

Heart Failure: Diagnosis, Management and Utilization.

Radio Frequency Identification (RFID) technology not only offers tracking capability to locate equipment, supplies and people in real time, but also provides efficient and accurate access to medical data for health professionals. However, the reality of RFID adoption in healthcare is far behind earlier expectation. This study reviews literature on the use of RFID in healthcare/hospitals following a formal innovation-decision framework. We aim to identify the common applications, potential benefits, barriers, and critical success factors. Our study facilitates quick assessment and provides guidance for researchers and practitioners in adopting RFID in medical arenas. Many earlier adopters in healthcare found RFID to be functional and useful in such areas as asset tracking and patient identification. Major barriers to adoption include technological limitations, interference concerns, prohibitive costs, lack of global standards and privacy concerns. Better designed RFID systems with low cost and privacy issues addressed are needed to increase acceptance of RFID in healthcare.

The Adoption and Implementation of RFID Technologies in Healthcare: A Literature Review

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

/pdf/prediction-of-heart-disease-using-a-combination-of-machine-541cl9iyi9.pdf

Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use To implement the smart intelligent functions, we adopted a machine learning approach In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case

A machine learning system to improve heart failure patient assistance.

https://flore.unifi.it/bitstream/2158/1191514/1/1-s2.0-S1746809419301776-main.pdf

A convolutional neural network approach to detect congestive heart failure

COVID-19 pandemic is plaguing the world and representing the most significant stress test for many national healthcare systems and services, since their foundation. The supply-chain disruption and the unprecedented request for intensive care unit (ICU) beds have created in Europe conditions typical of low-resources settings. This generated a remarkable race to find solutions for the prevention, treatment and management of this disease which is involving a large amount of people. Every day, new Do-It-Yourself (DIY) solutions regarding personal protective equipment and medical devices populate social media feeds. Many companies (e.g., automotive or textile) are converting their traditional production to manufacture the most needed equipment (e.g., respirators, face shields, ventilators etc.). In this chaotic scenario, policy makers, international and national standards bodies, along with the World Health Organization (WHO) and scientific societies are making a joint effort to increase global awareness and knowledge about the importance of respecting the relevant requirements to guarantee appropriate quality and safety for patients and healthcare workers. Nonetheless, ordinary procedures for testing and certification are currently questioned and empowered with fast-track pathways in order to speed-up the deployment of new solutions for COVID-19. This paper shares critical reflections on the current regulatory framework for the certification of personal protective equipment. We hope that these reflections may help readers in navigating the framework of regulations, norms and international standards relevant for key personal protective equipment, sharing a subset of tests that should be deemed essential even in a period of crisis.

/pdf/the-inadequacy-of-regulatory-frameworks-in-time-of-crisis-22xjxhxpwl.pdf

The Inadequacy of Regulatory Frameworks in Time of Crisis and in Low-Resource Settings: Personal Protective Equipment and COVID-19.

The medical device industry has grown rapidly and incessantly over the past century. The sophistication and complexity of the designed instrumentation is nowadays rising and, with it, has also increased the need to develop some better, more effective and efficient maintenance processes, as part of the safety and performance requirements. This paper presents the results of performance tests conducted on 50 mechanical ventilators and 50 infant incubators used in various public healthcare institutions. Testing was conducted in accordance to safety and performance requirements stated in relevant international standards, directives and legal metrology policies. Testing of output parameters for mechanical ventilators was performed in 4 measuring points while testing of output parameters for infant incubators was performed in 7 measuring points for each infant incubator. As performance criteria, relative error of output parameters for mechanical ventilators and absolute error of output parameters for infant incubators was calculated. The ranges of permissible error, for both groups of devices, are regulated by the Rules on Metrological and Technical Requirements published in the Official Gazette of Bosnia and Herzegovina No. 75/14, which are defined based on international recommendations, standards and guidelines. All ventilators and incubators were tested by etalons calibrated in an ISO 17025 accredited laboratory, which provides compliance to international standards for all measured parameters.The results show that 30% of the tested medical devices are not operating properly and should be serviced, recalibrated and/or removed from daily application.

Testing of mechanical ventilators and infant incubators in healthcare institutions.

This paper shows a method to get a patient tracking RFId solution, basing on a multilayer planning architecture. This approach is thought to guarantee that the found technical solution is as much as possible coherent to the very initial idea. Project aims, functional requirements and technical constraints are defined in order to arrive to an active RFId solution to track and identify patients inside a hospital. The article also deals with economical issues and physical design aspects. In this work it's also defined a three phases process for patient tracking, that could serve as a guideline for different technical solutions to the same problem.

Ernesto Iadanza

Papers

A machine learning system to improve heart failure patient assistance.

A convolutional neural network approach to detect congestive heart failure

The Inadequacy of Regulatory Frameworks in Time of Crisis and in Low-Resource Settings: Personal Protective Equipment and COVID-19.

Testing of mechanical ventilators and infant incubators in healthcare institutions.

Patients tracking and identifying inside hospital: A multilayer method to plan an RFId solution