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.

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 Diagnostic accuracy of a SARS-CoV-2 rapid antigen test in real-life clinical settings

2021 ◽  
Author(s):  
Sabrina Jegerlehner ◽  
Franziska Suter-Riniker ◽  
Philipp Jent ◽  
Pascal Bittel ◽  
Michael Nagler
Keyword(s):  
Diagnostic Accuracy ◽  
Real Life ◽  
Clinical Settings ◽  
Antigen Test

scholarly journals The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

2019 ◽  
Vol 63 (1) ◽  
pp. 1471-1475
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Human Factors ◽  
Cognitive Science ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
Prediction Markets ◽  
New Model ◽  
Large Groups

There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.

Exploring Association between Perceived Usability of Dosimetry Quality Assurance Checklist and Perceived Cognitive Workload of Dosimetrists in Clinical Settings

2021 ◽  
Vol 65 (1) ◽  
pp. 771-775
Author(s):  
Karthik Adapa ◽  
Prithima Mosaly ◽  
Fei Yu ◽  
Carlton Moore ◽  
Shiva Das ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Medical Errors ◽  
Healthcare Professionals ◽  
Medical Center ◽  
Academic Medical Center ◽  
Healthcare Delivery ◽  
Clinical Settings ◽  
Cognitive Workload ◽  
Contributing Factors ◽  
Perceived Usability

Usability and cognitive workload (CWL) are multidimensional constructs that describe user experience, predict performance, and inform system design. The relationship between the subjective measures of these constructs has not been adequately explored, especially in healthcare delivery settings where suboptimal usability of electronic health records and CWL of healthcare professionals are among the major contributing factors to medical errors. This study quantifies the perceived usability of a dosimetry quality assurance (QA) checklist and the perceived CWL of dosimetrists in radiation oncology clinical settings of an academic medical center and investigates the association between perceived usability and perceived CWL. Findings suggest that our institutional dosimetry QA checklist has suboptimal usability, but the associated CWL is acceptable. Further, the correlation analysis reveals that perceived usability and perceived CWL are non-overlapping constructs and may be jointly employed to reduce the risk of healthcare professionals committing medical errors.

scholarly journals Collectively Intelligent Teams: Integrating Team Cognition, Collective Intelligence, and AI for Future Teaming

2019 ◽  
Vol 63 (1) ◽  
pp. 1466-1470
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Collective Intelligence ◽  
Team Cognition ◽  
New Model ◽  
The Many ◽  
Large Groups

In this paper we propose a new model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. We do this by first characterizing what sets team cognition and collectively intelligence apart, and then reviewing the literature on “superforecasting” and the ability for effectively coordinated teams to outperform predictions by large groups. Lastly, we delve into the ways in which teamwork can be enhanced by artificial intelligence through our model, finally highlighting the many areas of research worth exploring through interdisciplinary efforts.

scholarly journals Boosting medical diagnostics by pooling independent judgments

2016 ◽  
Vol 113 (31) ◽  
pp. 8777-8782 ◽  
Author(s):  
Ralf H. J. M. Kurvers ◽  
Stefan M. Herzog ◽  
Ralph Hertwig ◽  
Jens Krause ◽  
Patricia A. Carney ◽  
...  
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Collective Intelligence ◽  
Medical Diagnostics ◽  
Wide Range ◽  
Key Factor ◽  
Systematic Effects ◽  
Real World Datasets ◽  
Skin Cancer Detection

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors’ diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches.

Error rates in physician dictation: quality assurance and medical record production

2014 ◽  
Vol 27 (2) ◽  
pp. 99-110 ◽  
Author(s):  
Gary C. David ◽  
Donald Chand ◽  
Balaji Sankaranarayanan
Keyword(s):  
Quality Assurance ◽  
Medical Record ◽  
Medical Errors ◽  
Medical Records ◽  
Error Rates ◽  
Content Type ◽  
Standard Work ◽  
Practical Implications ◽  
Made In

Purpose – The purpose of the paper is to determine the instance of errors made in physician dictation of medical records. Design/methodology/approach – Purposive sampling method was employed to select medical transcriptionists (MTs) as “experts” to identify the frequency and types of medical errors in dictation files. Seventy-nine MTs examined 2,391 dictation files during one standard work day, and used a common template to record errors. Findings – The results demonstrated that on the average, on the order of 315,000 errors in one million dictations were surfaced. This shows that medical errors occur in dictation, and quality assurance measures are needed in dealing with those errors. Research limitations/implications – There was no potential for inter-coder reliability and confirming the error codes assigned by individual MTs. This study only examined the presence of errors in the dictation-transcription model. Finally, the project was done with the cooperation of MTSOs and transcription industry organizations. Practical implications – Anecdotal evidence points to the belief that records created directly by physicians alone will have fewer errors and thus be more accurate. This research demonstrates this is not necessarily the case when it comes to physician dictation. As a result, the place of quality assurance in the medical record production workflow needs to be carefully considered before implementing a “once-and-done” (i.e. physician-based) model of record creation. Originality/value – No other research has been published on the presence of errors or classification of errors in physician dictation. The paper questions the assumption that direct physician creation of medical records in the absence of secondary QA processes will result in higher quality documentation and fewer medical errors.

scholarly journals Perceptions of Using Social Media as an ELT Tool among EFL Teachers in the Saudi Context

2016 ◽  
Vol 9 (7) ◽  
pp. 1 ◽  
Author(s):  
Madawi Allam ◽  
Tariq Elyas
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Foreign Language ◽  
Teaching And Learning ◽  
Quantitative Methods ◽  
Collective Intelligence ◽  
Tertiary Institutions ◽  
Media Technologies ◽  
Efl Teachers ◽  
Huge Impact

<p>Social media technologies have undeniably become an integral part of people’s lives and they have been widely used amonsgest the new genrations, particularly, university students. This widespread of social media technologies has certainly made a huge impact on the way people learn and interact with each other resulting in the emergence of communities of learning that are supported by collective intelligence. This study is based on quantitative methods using a survey instrument to gather descriptive data regarding the perceptions of seventy-five (<em>n=75) </em>randomly chosen male and female English as a Foreign Language (EFL) teachers at two Saudi tertiary institutions. The study utilized a 14 Likert scale statements where each statement had five Likert-type items for the participants to choose from. Analysis of the gathered data indicated that the majority of the participants believe strongly in the pedagoocal values and benefits of using social media as an ELT tool in the EFL classes in the Saudi context. Nevertheless, the majority expressed reservations with regards to the extent to which social media can be freely allowed to be used in the EFL classroom where they perceive it as having a double edged sword effect and that is mainly due to some undesired distractions that some students may resort to which may occasionaly result in the opposite of the intended effect of their usage. The study recommends more research studies in this area so as to closely understand how experienced EFL teachers utilize social media in their classes in order to develop best practices for implementing social media in teaching and learning in EFL in the Saudi contexts</p>

scholarly journals QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

2020 ◽  
Vol 10 (19) ◽  
pp. 6896
Author(s):  
Paloma Tirado-Martin ◽  
Judith Liu-Jimenez ◽  
Jorge Sanchez-Casanova ◽  
Raul Sanchez-Reillo
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
State Of The Art ◽  
Computational Cost ◽  
Extraction Process ◽  
Error Rates ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Standing Up ◽  
Electrocardiogram Ecg

Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively.

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