scholarly journals Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 105
Author(s):  
Khaleel Husain ◽  
Mohd Soperi Mohd Zahid ◽  
Shahab Ul Hassan ◽  
Sumayyah Hasbullah ◽  
Satria Mandala
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Privacy Preservation ◽  
Causes Of Death ◽  
Clinical Settings ◽  
Data Format ◽  
Medical Institutions ◽  
Comprehensive Survey ◽  
Electrocardiogram Ecg ◽  
The Relationship

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

scholarly journals The first Russian nomenclature of diseases and diagnostics of causes of death in the XIX century: a particular case

2018 ◽  
Vol 2 (4) ◽  
pp. 1-46
Author(s):  
Irina Troitskaia ◽  
Alexander Avdeev
Keyword(s):  
Causes Of Death ◽  
Local Population ◽  
Medical Society ◽  
Information Base ◽  
Moscow Province ◽  
Medical Institutions ◽  
Definition Of ◽  
Xix Century ◽  
Relationship Of ◽  
The Relationship

The purpose of this article is to analyze changes in the diagnosis of causes of death of the local population and to study the relationship of these changes with the development of medicine and unification of the definition of causes of death in Russia. The information base of the study is the registers of the two parishes in the Moscow County in the period from 1815 to 1918. The obtained results show a significant improvement in the diagnosing of causes of death in the second half of the XIX century, connected with the expansion of the network of medical institutions in the Moscow Province and the activity of the medical society in the development of the Russian nomenclature of diseases.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 1-26
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 403-420
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals The relationship between trust in AI and trustworthy machine learning technologies

2020 ◽  
Author(s):  
Ehsan Toreini ◽  
Mhairi Aitken ◽  
Kovila Coopamootoo ◽  
Karen Elliott ◽  
Carlos Gonzalez Zelaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
The Relationship

Age-Dependency of Total Tau in the Cerebrospinal Fluid Is Corrected by Amyloid-β 1–40: A Correlational Study in Healthy Adults

2021 ◽  
pp. 1-8
Author(s):  
Paul Theo Zebhauser ◽  
Achim Berthele ◽  
Marie-Sophie Franz ◽  
Oliver Goldhardt ◽  
Janine Diehl-Schmid ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Amyloid Β ◽  
Healthy Adults ◽  
Tau Proteins ◽  
Clinical Settings ◽  
Inclusion Criteria ◽  
Total Tau ◽  
Potential Marker ◽  
Wide Range ◽  
The Relationship

Background: Tau proteins are established biomarkers of neuroaxonal damage in a wide range of neurodegenerative conditions. Although measurement of total-Tau in the cerebrospinal fluid is widely used in research and clinical settings, the relationship between age and total-Tau in the cerebrospinal fluid is yet to be fully understood. While past studies reported a correlation between age and total-Tau in the cerebrospinal fluid of healthy adults, in clinical practice the same cut-off value is used independently of patient’s age. Objective: To further explore the relationship between age and total-Tau and to disentangle neurodegenerative from drainage-dependent effects. Methods: We analyzed cerebrospinal fluid samples of 76 carefully selected cognitively healthy adults and included amyloid-β 1–40 as a potential marker of drainage from the brain’s interstitial system. Results: We found a significant correlation of total-Tau and age, which was no longer present when correcting total-Tau for amyloid-β 1–40 concentrations. These findings were replicated under varied inclusion criteria. Conclusion: Results call into question the association of age and total-Tau in the cerebrospinal fluid. Furthermore, they suggest diagnostic utility of amyloid-β 1–40 as a possible proxy for drainage-mechanisms into the cerebrospinal fluid when interpreting biomarker concentrations for neurodegenerative diseases.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology

2021 ◽  
Vol 9 (2) ◽  
pp. 17
Author(s):  
Luca Ronzio ◽  
Andrea Campagner ◽  
Federico Cabitza ◽  
Gian Franco Gensini
Keyword(s):  
Diagnostic Accuracy ◽  
Medical Errors ◽  
Collective Intelligence ◽  
Error Rates ◽  
Clinical Settings ◽  
Huge Impact ◽  
Recent Graduates ◽  
Human Costs ◽  
Electrocardiogram Ecg ◽  
Large Groups

Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing interest as a means of reducing error rates and their impacts. Previous studies have shown that a combination of individual opinions based on rules, weighting mechanisms, or other CI solutions could improve diagnostic accuracy with respect to individual doctors. We conducted a study to investigate the potential of this approach in cardiology and, more precisely, in electrocardiogram (ECG) reading. To achieve this aim, we designed and conducted an experiment involving medical students, recent graduates, and residents, who were asked to annotate a collection of 10 ECGs of various complexity and difficulty. For each ECG, we considered groups of increasing size (from three to 30 members) and applied three different CI protocols. In all cases, the results showed a statistically significant improvement (ranging from 9% to 88%) in terms of diagnostic accuracy when compared to the performance of individual readers; this difference held for not only large groups, but also smaller ones. In light of these results, we conclude that CI approaches can support the tasks mentioned above, and possibly other similar ones as well. We discuss the implications of applying CI solutions to clinical settings, such as cases of augmented ‘second opinions’ and decision-making.

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