The Expertise Level

2020 ◽  
pp. 49-68
Author(s):  
Ron Fulbright
Keyword(s):  
Expertise Level

scholarly journals A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

2021 ◽  
Vol 18 (4) ◽  
pp. 2121
Author(s):  
Mansoureh Maadi ◽  
Hadi Akbarzadeh Khorshidi ◽  
Uwe Aickelin
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Computational Power ◽  
Medical Field ◽  
Interactive Machine Learning ◽  
Human In The Loop ◽  
Human Interactions ◽  
Scoping Literature Review ◽  
Domain Expertise ◽  
Expertise Level

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

How Do Cardiology Practitioners Interpret an Electrocardiogram: An Eye Tracking Study (Preprint)

2021 ◽  
Author(s):  
Mohammed Tahri Sqalli ◽  
Dena Al-Thani ◽  
Mohamed Badreldin Elshazly ◽  
Mohammed Ahmad Al-Hijji ◽  
Yahya Sqalli Houssaini
Keyword(s):  
Standard Deviation ◽  
Medical Students ◽  
Eye Tracking ◽  
Practical Experience ◽  
Healthcare Sector ◽  
Visual Expertise ◽  
Medical Practitioners ◽  
Eye Fixations ◽  
Expertise Level ◽  
Ecg Leads

BACKGROUND Visual expertise refers to advanced visual skills demonstrated when executing domain‐specific visual tasks. Understanding healthcare practitioners’ visual expertise across different levels in the healthcare sector is crucial in clarifying how to acquire accurate interpretations of electrocardiograms (ECGs). OBJECTIVE The study aims to quantify, through the use of eye-tracking, differences in the visual expertise of medical practitioners, such as medical students, cardiology nurses, technicians, fellows, and consultants, when interpreting ECGs. METHODS Sixty-three participants with different healthcare roles participated in an eye-tracking study that consisted of interpreting 10 ECGs with different heart abnormalities. A counterbalanced within-subjects design was employed with one independent variable consisting of the expertise level of the medical practitioners and two measured eye-tracking dependent variables (fixations count and fixations revisitation). Eye-tracking data was assessed according to the accuracy of interpretation and frequency interpreters visited different leads in ECGs. In addition, the median and standard deviation in the interquartile range for the fixations count and the mean and standard deviation for the ECG lead revisitations were calculated. RESULTS Accuracy of interpretation ranged between 98% among consultants and 52% among medical students. Eye-tracking features also reflected this difference in the accuracy of interpretation. The results of the eye fixations count and eye fixations revisitations indicate that the less experienced medical practitioners need to observe various ECG leads more carefully. However, experienced medical practitioners rely on visual pattern recognition to provide their ECG diagnoses. CONCLUSIONS The results show that visual expertise for ECG interpretation is linked to the practitioner’s role within the healthcare system and the number of years of practical experience interpreting ECGs. Medical practitioners focus on different ECG leads and different waveform abnormalities according to their role in the healthcare sector and their expertise levels.

scholarly journals Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations

2020 ◽  
Vol 34 (03) ◽  
pp. 2518-2526
Author(s):  
Sarath Sreedharan ◽  
Tathagata Chakraborti ◽  
Christian Muise ◽  
Subbarao Kambhampati
Keyword(s):  
Decision Making ◽  
Complete Solution ◽  
Robot Behavior ◽  
Computational Advantage ◽  
User Expectations ◽  
Expertise Level ◽  
Planning Problems

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.

Association between the Expertise Level and Experience of Clinical Research Coordinators

2020 ◽  
Vol 51 (2) ◽  
pp. 71-76
Author(s):  
Izumi KOHARA ◽  
Masako TSURUMARU ◽  
Noriko MORISHITA ◽  
Nobuko USHIROZAWA ◽  
Hiroi KASAI ◽  
...  
Keyword(s):  
Clinical Research ◽  
Clinical Research Coordinators ◽  
Expertise Level

scholarly journals Supplier selection and consistency level measurement of decision maker using AHP method and expertise level model

2018 ◽  
Vol 204 ◽  
pp. 02013
Author(s):  
Fatkhor Rozi ◽  
Evy Herowati
Keyword(s):  
Decision Maker ◽  
Supplier Selection ◽  
Raw Materials ◽  
Selection Process ◽  
Manufacturing Companies ◽  
Ahp Method ◽  
Level Measurement ◽  
Level Model ◽  
Expertise Level ◽  
The Right

Raw materials are basic need for manufacturing companies to facilitate the production process. Therefore, supplier selection process is an important process for the company, because by choosing the right suppliers of raw materials will benefit the company. To choose the right supplier is also determined by the decision taken by the decision maker (DM). In addition, the weight of DM assessment will differ on different criteria because DM experts in a particular field are not necessarily experts in other fields. Therefore, in this paper is discussed about the measurement of the weight of each criterion and alternative to choose the best supplier alternative using AHP method. In addition, this paper also measures the consistency of DM in making decisions using expertise level models.

scholarly journals Sensitivity and Specificity of Ultrawide-Field Fundus Photography for the Staging of Sickle Cell Retinopathy in Real-Life Practice at Varying Expertise Level

2019 ◽  
Vol 8 (10) ◽  
pp. 1660 ◽  
Author(s):  
Roxane Bunod ◽  
Alexandra Mouallem-Beziere ◽  
Francesca Amoroso ◽  
Vittorio Capuano ◽  
Karen Bitton ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Sickle Cell ◽  
Real Life ◽  
Retinal Disease ◽  
Fundus Photography ◽  
Cell Disease ◽  
Real Life Setting ◽  
Expertise Level ◽  
Retinal Photocoagulation

Purpose: To evaluate the sensitivity and specificity of ultrawide-field fundus photography (UWF-FP) for the detection and classification of sickle cell retinopathy (SCR) by ophthalmologists with varying degrees of expertise in retinal disease. Methods: Patients presenting with sickle cell disease (SCD) in the Créteil University Eye Clinic, having undergone UWF-FP and ultrawide-field fluorescein angiography (UWF-FA) on the same day, were retrospectively included. Eyes with previous retinal photocoagulation were excluded. SCR was graded independently by UWF-FP and UWF-FA using Goldberg classification by two ophthalmologists with varying expertise levels. Results: Sixty-six eyes of 33 patients were included in the study. The sensitivity of UWF-FP for the detection of proliferative SCR was 100%, (95% confidence interval [CI95%] 76.8–100) for the retinal specialist and 100% (CI95% 71.5–100) for the ophthalmology resident. The specificity of UWF-FP for the detection of proliferative SCR was 100% (CI95% 92.7–100) for the retinal specialist and 98.1% (CI95% 89.7–100) for the ophthalmology resident. Conclusions: UWF-FP is a valuable exam for proliferative SCR screening, with excellent sensitivity and specificity and a good inter-grader agreement for ophthalmologists with various degree of skills, and is easy to use in a real-life setting.

scholarly journals Identifying the Most Common CrossFit Injuries in a Variety of Athletes

2020 ◽  
Vol 9 ◽  
pp. 117957271989706
Author(s):  
Kirill Alekseyev ◽  
Alex John ◽  
Andrew Malek ◽  
Malcolm Lakdawala ◽  
Nikhil Verma ◽  
...  
Keyword(s):  
Risk Factors ◽  
Large Scale ◽  
The United States ◽  
Cross Sectional Study ◽  
Musculoskeletal Injuries ◽  
Online Forums ◽  
Cross Sectional ◽  
Advanced Level ◽  
Expertise Level ◽  
Associated Risk Factors

Background: CrossFit is an increasingly popular, rapidly growing exercise regimen. Few studies have evaluated CrossFit-associated musculoskeletal injuries on a large scale. This study explores such injuries and associated risk factors in detail. Objective: To identify the most common musculoskeletal injuries endured during CrossFit training among athletes at different levels of expertise. Design: Survey-based retrospective cross-sectional study. Setting: Distribution at CrossFit gyms in the United States and internationally. Also published on active online forums. Participants: A total of 885 former and current CrossFit athletes. Methods: Institutional review board-approved 33-question Web-based survey focused on CrossFit injuries and associated risk factors. Survey submissions were accepted for a period of 6 months. Main outcome measurements: Specific injuries with associated workouts, risk factors that affected injury including (1) basic demographics, (2) regional differences in reported injuries, (3) training intensity, and (4) expertise level at time of injury. Results: Of the 885 respondents, 295 (33.3%) were injured. The most common injuries involved the back (95/295, 32.2%) and shoulder (61/295, 20.7%). The most common exercises that caused injury were squats (65/295, 22.0%) and deadlifts (53/295, 18.0%). Advanced-level (64/295, 21.7%) athletes were more significantly injured than beginner-level (40/295, 13.6%) athletes. International participants were 2.2 times more likely than domestic US participants to suffer injury. Individuals with 3+ years of CrossFit experience were 3.3 times more likely to be injured than those with 2 or less years of experience. Participants who trained for 11+ h/week were significantly more likely to be injured than those who trained less than or equal to 10 h/week. Conclusions: As CrossFit becomes more popular, it is important to monitor the safety of its practitioners. Further studies are needed to explore how to lower this injury prevalence of 33.3%. Areas to focus on include factors that have caused the regional (international vs US states) differences, level of expertise/experience differences (advanced level vs intermediate and beginner levels), and stretching routine modifications.

Developing the Assessment Questions Automatically to Determine the Cognitive Level of the E-Learner Using NLP Techniques

2020 ◽  
Vol 11 (2) ◽  
pp. 95-110
Author(s):  
Deena G. ◽  
Raja K. ◽  
Nizar Banu P.K. ◽  
Kannan K.
Keyword(s):  
Language Processing ◽  
Vital Role ◽  
Multiple Choice Questions ◽  
Digital World ◽  
Online Mode ◽  
E Learning ◽  
Computer Based ◽  
Expertise Level ◽  
Teaching Learning ◽  
Assessment Questions

The key objective of the teaching-learning process (TLP) is to impart the knowledge to the learner. In the digital world, the computer-based system emphasis teaching through online mode known as e-learning. The expertise level of the learner in learned subjects can be measured through e-assessment in which multiple choice questions (MCQ) is considered to be an effective one. The assessment questions play the vital role which decides the ability level of a learner. In manual preparation, covering all the topics is difficult and time consumable. Hence, this article proposes a system which automatically generates two different types of question helps to identify the skill level of a learner. First, the MCQ questions with the distractor set are created using named entity recognizer (NER). Further, based on blooms taxonomy the Subjective questions are generated using natural language processing (NLP). The objective of the proposed system is to generate the questions dynamically which helps to reduce the occupation of memory concept.

scholarly journals Algorithm-Aided Design with Python: Analysis of Technological Competence of Subjects

2018 ◽  
Vol 8 (4) ◽  
pp. 200 ◽  
Author(s):  
Thiago Pontes ◽  
Guilhermina Miranda ◽  
Gabriela Celani
Keyword(s):  
Instructional Design ◽  
Achievement Motivation ◽  
Scientific Literature ◽  
Online Environment ◽  
Know How ◽  
Technological Competence ◽  
Academic Choice ◽  
Expertise Level ◽  
Programming Knowledge ◽  
Aided Design

Difficulties in learning computer programming for novices is a subject of abundant scientific literature. These difficulties seem to be accentuated in students whose academic choice is not computation, like architecture students. However, they need to study programming, since it is part of the new academic curricula. The results presented here are part of a PhD research, which investigates the achievement motivation and the acquisition and transfer of programming knowledge from an online environment designed on the basis of the 4C-ID instructional design model. These results are a sociodemographic analysis, and the technological competence of these subjects. We concluded that most of the students of our sample do not know how to auto assess their ICT expertise level, because they believed that they had sufficient computational knowledge for their needs. However, most of them told that they had difficulties creating codes. However, they recognized the importance of learning to program, thought it was valuable for architectural students, and felt motivated to acquire this new skill.

scholarly journals Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yu Li ◽  
Hongfei Cao ◽  
Carla M. Allen ◽  
Xin Wang ◽  
Sanda Erdelez ◽  
...  
Keyword(s):  
Visual Attention ◽  
Computational Method ◽  
Human Cognition ◽  
Visual Knowledge ◽  
Spatial Features ◽  
Temporal Features ◽  
Visual Tasks ◽  
Visual Understanding ◽  
Reasoning Patterns ◽  
Expertise Level

AbstractVisual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

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