scholarly journals Cross-platform digital assessment forms for evaluating surgical skills

2015 ◽  
Vol 12 ◽  
pp. 13
Author(s):  
Steven Arild Wuyts Andersen
Keyword(s):  
Data Processing ◽  
Surgical Skills ◽  
Assessment Tools ◽  
Handheld Devices ◽  
Web Browsers ◽  
Digital Assessment ◽  
Desktop Computers ◽  
Cross Platform ◽  
Database Platform ◽  
Further Development

A variety of structured assessment tools for use in surgical training have been reported, but extant assessment tools often employ paper-based rating forms. Digital assessment forms for evaluating surgical skills could potentially offer advantages over paper-based forms, especially in complex assessment situations. In this paper, we report on the development of cross-platform digital assessment forms for use with multiple raters in order to facilitate the automatic processing of surgical skills assessments that include structured ratings. The FileMaker 13 platform was used to create a database containing the digital assessment forms, because this software has cross-platform functionality on both desktop computers and handheld devices. The database is hosted online, and the rating forms can therefore also be accessed through most modern web browsers. Cross-platform digital assessment forms were developed for the rating of surgical skills. The database platform used in this study was reasonably priced, intuitive for the user, and flexible. The forms have been provided online as free downloads that may serve as the basis for further development or as inspiration for future efforts. In conclusion, digital assessment forms can be used for the structured rating of surgical skills and have the potential to be especially useful in complex assessment situations with multiple raters, repeated assessments in various times and locations, and situations requiring substantial subsequent data processing or complex score calculations.

The Promise and Problem of E-Assessment

2020 ◽  
pp. 299-313
Author(s):  
Michael D. Franzen ◽  
Matthew J. L. Page
Keyword(s):  
Psychological Assessment ◽  
Ecological Validity ◽  
Handheld Devices ◽  
Great Promise ◽  
Research Environment ◽  
New Methods ◽  
Desktop Computers ◽  
Commercial Environment

There has been an explosion in the development of electronic methods for psychological assessment. These include use of handheld devices, desktop computers, and platform-based Internet methods. This development has occurred separately in the commercial environment and in the research environment. This development of new methods presents great promise to improve the accuracy, ecological validity, and range of constructs in psychological assessment. However there are also many problems involved in the development of these electronic methods, including the need to train clinicians in their use, the need to develop safeguards for privacy, and the need to develop methods to ensure the integrity of the data collected. This chapter outlines some of the main considerations in moving forward.

A Systematic Review of the Current State and Validity of Digital Assessment Tools for Psychiatry (Preprint)

2021 ◽  
Author(s):  
Nayra Anna Martin-Key ◽  
Benedetta Spadaro ◽  
Erin Funnell ◽  
Eleanor Jane Barker ◽  
Thea Sofie Schei ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Depressive Disorder ◽  
Diagnostic Accuracy ◽  
Mental Healthcare ◽  
Risk Of Bias ◽  
Assessment Tools ◽  
Cochrane Library ◽  
Digital Assessment ◽  
Current State

BACKGROUND Given the role digital technologies are likely to play in the future of mental healthcare, there is a need for a comprehensive appraisal of the current state and validity (i.e., screening/diagnostic accuracy) of digital mental health assessments. OBJECTIVE To explore the current state and validity of question-and-answer-based digital tools for diagnosing and screening psychiatric conditions in adults. METHODS This systematic review was based on the Population, Intervention, Comparison, and Outcome (PICO) framework and was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO were systematically searched for articles published between 2005 and 2020. A descriptive evaluation of the study characteristics and digital solutions and a quantitative appraisal of the screening/diagnostic accuracy of the included tools was conducted. Risk of bias and applicability were assessed using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) guidelines. RESULTS A total of 25 studies met the inclusion criteria, with the most frequently evaluated conditions encompassing generalized anxiety disorder (GAD), major depressive disorder (MDD), and any depressive disorder. The majority of the studies employed digitized versions of existing pen-and-paper questionnaires, with findings revealing poor to excellent screening/diagnostic accuracy (sensitivity = 0.36-1.00, specificity = 0.37-1.00, AUC = 0.57-0.98) and a high risk of bias for most of the included studies. CONCLUSIONS The current state of the field of digital mental health tools is in its early stages and high-quality evidence is lacking. INTERNATIONAL REGISTERED REPORT RR2-10.2196/25382

Open source column: Deep learning in the browser

2019 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Sabrina Kletz ◽  
Marco Bertini ◽  
Mathias Lux
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Phone ◽  
Open Source ◽  
Web Pages ◽  
Web Browsers ◽  
Open Source Framework ◽  
Interesting Approach ◽  
Cross Platform ◽  
Client Device

Having already discussed MatConvNet and Keras, let us continue with an open source framework for deep learning, which takes a new and interesting approach. TensorFlow.js is not only providing deep learning for JavaScript developers, but it's also making applications of deep learning available in the WebGL enabled web browsers, or more specifically, Chrome, Chromium-based browsers, Safari and Firefox. Recently node.js support has been added, so TensorFlow.js can be used to directly control TensorFlow without the browser. TensorFlow.js is easy to install. As soon as a browser is installed one is ready to go. Browser based, cross platform applications, e.g. running with Electron, can also make use of TensorFlow.js without an additional install. The performance, however, depends on the browser the client is running, and memory and GPU on the client device. More specifically, one cannot expect to analyze 4K videos on a mobile phone in real time. While it's easy to install, and it's easy to develop based on TensorFlow.js, there are drawbacks: (i) developers have less control over where the machine learning actually takes place (e.g. on CPU or GPU), that it is running in the same sandbox as all web pages in the browser do, and (ii) that in the current release it still has rough edges and is not considered stable enough to use in production.

scholarly journals ssNake: A cross-platform open-source NMR data processing and fitting application

2019 ◽  
Vol 301 ◽  
pp. 56-66 ◽  
Author(s):  
S.G.J. van Meerten ◽  
W.M.J. Franssen ◽  
A.P.M. Kentgens
Keyword(s):  
Data Processing ◽  
Open Source ◽  
Nmr Data ◽  
Cross Platform

Service-Oriented Architecture of Distributed Telemetered Data Processing

2014 ◽  
Vol 644-650 ◽  
pp. 2568-2571
Author(s):  
Li Bing Guo ◽  
Jin Biao Zhou ◽  
Yong Gang Li ◽  
Lin Qi Zhou ◽  
Sheng Ping Li
Keyword(s):  
Data Processing ◽  
Service Oriented Architecture ◽  
Service Integration ◽  
Trajectory Data ◽  
Distributed Environment ◽  
Architecture Model ◽  
Deployment Strategy ◽  
Service Oriented ◽  
Cross Platform ◽  
Distributed Deployment

This paper introduces the service-oriented architecture technology, and proposes the architecture model of Out-trajectory Data Processing in distributed environment. According to the demand and features of Ship-borne Out-trajectory Data Processing flow, it describes how to carry out cross-platform service integration by using the Service Bus Middleware, and provides a kind of typical distributed deployment strategy.

scholarly journals A Systematic Review of the Current State and Validity of Digital Assessment Tools for Psychiatry (Preprint)

10.2196/32824 ◽  
2021 ◽  
Author(s):  
Nayra A Martin-Key ◽  
Benedetta Spadaro ◽  
Erin Funnell ◽  
Eleanor Jane Barker ◽  
Thea Sofie Schei ◽  
...  
Keyword(s):  
Systematic Review ◽  
Assessment Tools ◽  
Digital Assessment ◽  
Current State

scholarly journals Conclusions and Outlook—Summary of Big Data in Forestry

2021 ◽  
pp. 363-367
Author(s):  
Jukka Miettinen ◽  
Renne Tergujeff
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Service Providers ◽  
Data Service ◽  
Big Data Processing ◽  
Practical Applications ◽  
Service Chain ◽  
Further Development

AbstractIn this chapter, we summarize the findings from the forestry pilots conducted during the DataBio project. Although the pilots demonstrated the functionality of big data in forestry through several practical applications and services, they also highlighted areas where further development is needed. More effort is needed particularly in ensuring smooth connections between the technical components of the processing pipelines, as well as designing the best business solutions within the big data service chain and between the service providers and users. Overall, the challenge for the coming years is to establish operational big data processing pipelines that meet the requirements and expectations of forestry stakeholders.

Surgical skills assessment tools in gynecology

2018 ◽  
pp. 1
Author(s):  
Nisse V. Clark ◽  
Kristen J. Pepin ◽  
Jon I. Einarsson
Keyword(s):  
Surgical Skills ◽  
Assessment Tools ◽  
Skills Assessment

An MPEG-DASH Methodology for QoE-Aware Web3D Streaming

2014 ◽  
Vol 3 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Kostas Kapetanakis ◽  
Markos Zampoglou ◽  
Athanasios G. Malamos ◽  
Spyros Panagiotakis ◽  
Emmanuel Maravelakis
Keyword(s):  
3D Models ◽  
3D Graphics ◽  
Adaptive Streaming ◽  
Technological Infrastructure ◽  
Web Technologies ◽  
Desktop Computers ◽  
Ubiquitous Environment ◽  
Wide Range ◽  
The Face ◽  
Cross Platform

Recent advances in web technologies have now created a ubiquitous environment for cross-platform and cross-device multimedia applications. Media files can now be reproduced in a wide range of devices, from mobile phones to desktop computers and web-enabled televisions, using a common infrastructure. This trend towards unifying the technological infrastructure, however, has given rise to a new array of problems resulting from the varying technological capabilities of the different devices and environments. This paper, proposes an adaptive streaming framework for the display of 3D models on a wide range of web-enabled devices. The open, XML-based X3D language for 3D graphics is combined with the MPEG-DASH standard for adaptive streaming. The end result is a framework that can adaptively display 3D graphics in the face of network or computational limitations, and dynamically adapt data flow to maximize user Quality of Experience in any situation.

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