Machine Learning-as-a-Service and Its Application to Medical Informatics

2017 ◽  
pp. 206-219 ◽  
Author(s):  
Ahmad P. Tafti ◽  
Eric LaRose ◽  
Jonathan C. Badger ◽  
Ross Kleiman ◽  
Peggy Peissig
Keyword(s):  
Machine Learning ◽  
Medical Informatics

Machine Learning and Data Analytics in Pervasive Health

2018 ◽  
Vol 57 (04) ◽  
pp. 194-196
Author(s):  
Nuria Oliver ◽  
Michael Marschollek ◽  
Oscar Mayora
Keyword(s):  
Machine Learning ◽  
Medical Informatics ◽  
Data Analytics ◽  
Peer Review Process ◽  
Personalised Medicine ◽  
Physiological Data ◽  
European Federation ◽  
Small Data ◽  
Health Technologies ◽  
Pervasive Health

Summary Introduction: This accompanying editorial provides a brief introduction to this focus theme, focused on “Machine Learning and Data Analytics in Pervasive Health”. Objective: The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes. Methods: A call for paper was announced to all participants of the “11th International Conference on Pervasive Computing Technologies for Healthcare”, to different working groups of the International Medical Informatics Association (IMIA) and European Federation of Medical Informatics (EFMI) and was published in June 2017 on the website of Methods of Information in Medicine. A peer review process was conducted to select the papers for this focus theme. Results: Four papers were selected to be included in this focus theme. The paper topics cover a broad range of machine learning and data analytics applications in healthcare including detection of injurious subtypes of patient-ventilator asynchrony, early detection of cognitive impairment, effective use of small data sets for estimating the performance of radiotherapy in bladder cancer treatment, and the use negation detection in and information extraction from unstructured medical texts. Conclusions: The use of machine learning and data analytics technologies in healthcare is facing a renewed impulse due to the availability of large amounts and new sources of human behavioral and physiological data, such as that captured by mobile and pervasive devices traditionally considered as nonmainstream for healthcare provision and management.

A Special Section on Advanced Computing Techniques for Machine Learning and Data Mining in Medical Informatics

2016 ◽  
Vol 6 (4) ◽  
pp. 1052-1055 ◽  
Author(s):  
Kelvin K. L. Wong ◽  
Defeng Wang ◽  
Simon Fong ◽  
Eddie Y. K. Ng
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Medical Informatics ◽  
Special Section ◽  
Advanced Computing

Machine learning-enabled Internet of Things for medical informatics

2021 ◽  
pp. 111-126
Author(s):  
Ali Nauman ◽  
Yazdan Ahmad Qadri ◽  
Rashid Ali ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Medical Informatics

scholarly journals Argumentation and explainable artificial intelligence: a survey

2021 ◽  
Vol 36 ◽  
Author(s):  
Alexandros Vassiliades ◽  
Nick Bassiliades ◽  
Theodore Patkos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Medical Informatics ◽  
Predictive Models ◽  
Web Security ◽  
General Purpose ◽  
Conflicting Information ◽  
Explainable Artificial Intelligence ◽  
Combine Machine

Abstract Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

scholarly journals Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

2021 ◽  
Vol 30 (01) ◽  
pp. 185-190
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Knowledge Representation ◽  
Medical Informatics ◽  
Open Science ◽  
Research Interest ◽  
Health Records ◽  
Knowledge Representations ◽  
Research Areas ◽  
Significant Research

Summary Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines. Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented. Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

scholarly journals An experimental methodology to capture user and gameplay data tied to cybersickness

2021 ◽  
Author(s):  
Thiago Porcino ◽  
Daniela Trevisan ◽  
Esteban Clua
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Medical Informatics ◽  
Experimental Methodology ◽  
Head Mounted Displays ◽  
Recorded Data ◽  
Learning Analysis ◽  
High Sense

Virtual reality (VR) and head-mounted displays are constantly gaining popularity in various fields such as education, military, entertainment, and bio/medical informatics. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent publications in the virtual reality context. We created and conducted an iterative evaluating protocol methodology and proposed two VR games (a racing game and a flight game). The recorded data can be used for further machine learning analysis tied to cybersickness.

scholarly journals Automated Creation of Expert Systems with the InteKRator Toolbox

2021 ◽  
Author(s):  
Daan Apeldoorn ◽  
Torsten Panholzer
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Medical Informatics ◽  
Learning Approaches ◽  
Artificial Intelligence Research ◽  
New Evidence

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning.

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