Accessibility in M-Learning

Roberts, Jodi

doi:10.4324/9780203118764.ch37

Cited by 4 publications

(6 citation statements)

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“…Jasper's strengths in creative language processing and text analysis make it a promising tool for extracting biomarkers from various medical sources [22]. While not specifically trained for medical tasks, its flexibility and adaptability offer exciting possibilities in this domain.…”

Section: Jasper Chatmentioning

confidence: 99%

“…It delves into physician observations, progress notes, and discharge summaries, understanding context and relationships between words. This enables Jasper to identify hidden patterns and associations between clinical features and specific diseases [22]. For instance, it might uncover links between chronic cough mentions and elevated sputum eosinophil counts, suggesting potential correlations for diseases like allergic bronchopulmonary aspergillosis.…”

Section: Extracting Biomarkers From Ehr Notesmentioning

confidence: 99%

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Navigating the Medical Landscape: A Review of Chatbots for Biomarker Extraction from Diverse Medical Reports

et al. 2024

IJFMR

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This research investigates the potential of chatbots to revolutionize the extraction of biomarkers from a variety of medical reports, such as pathology reports, CT scans, and Electronic Health Record (EHR) notes. The research sheds light on the histories, training approaches, and uses of popular chatbots including ChatGPT, Google Bard, and Jasper Chat through a comparative examination, providing insights into their efficacy in healthcare settings. Issues with data privacy, complicated integration, ethical issues, and technological constraints are explored, illuminating the challenging terrain of deploying chatbots for biomarker extraction. The ramifications for healthcare are discussed as the research comes to a close, with a focus on the possibility of better patient outcomes, more accurate diagnosis, and the development of tailored therapy. Future research recommendations emphasize the continued need for algorithm optimization, innovative integration exploration, and ethical considerations.

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Section: Jasper Chatmentioning

confidence: 99%

Section: Extracting Biomarkers From Ehr Notesmentioning

confidence: 99%

Navigating the Medical Landscape: A Review of Chatbots for Biomarker Extraction from Diverse Medical Reports

et al. 2024

IJFMR

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“…More sophisticated approaches compute explicit schedules. Roberts and Howe (2006) rank the portfolio algorithms in order of expected performance and allocate time according to this ranking. Pulina and Tacchella (2009) investigate different ways of computing schedules and conclude that ordering the algorithms based on their past performance and allocating the same amount of time to all algorithms gives the best overall performance.…”

Section: Problem Solving With Portfoliosmentioning

confidence: 99%

Algorithm Selection for Combinatorial Search Problems: A Survey

2014

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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.

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“…In the area of teaching and learning in higher education, mobile computing technologies have become a very important research topic over the past few years (see, e.g., [5][6][7][8]). The use of mobile devices for educational purposes led to what is known as mobile learning, providing new opportunities for active learning by utilizing the advantages of mobility and wireless technology [7,8].…”

Section: Mobile Learning In Higher Educationmentioning

confidence: 99%

A Course Design for Individualized Learning Leveraging Didactic Functions of Mobile Learning Devices

Schefer-Wenzl¹,

Miladinović²

2017

Int. J. Adv. Corp. Learn.

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Abstract-Mobile computing and wireless technologies are transforming traditional educational patterns. The use of mobile devices for educational purposes led to what is known as mobile learning. In recent years, mobile learning strategies have been increasingly adopted in higher education. They support personalized, informal, situated, contextualized and ubiquitous learning. In practice, we have witnessed that courses in software engineering education often do not lead to the expected learning outcomes. In this paper, we will therefore present a motivating and learner-centered approach for mobile e-learning in application prototyping. We use mobile devices as key enabler serving different didactic functions in the context of learning, prototyping and collaboration.

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Accessibility in M-Learning

Cited by 4 publications

References 0 publications

Navigating the Medical Landscape: A Review of Chatbots for Biomarker Extraction from Diverse Medical Reports

Navigating the Medical Landscape: A Review of Chatbots for Biomarker Extraction from Diverse Medical Reports

Algorithm Selection for Combinatorial Search Problems: A Survey

A Course Design for Individualized Learning Leveraging Didactic Functions of Mobile Learning Devices

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