Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text.)
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'''"[[Journal:A legal framework to support development and assessment of digital health services|A legal framework to support development and assessment of digital health services]]"'''
'''"[[Journal:Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine|Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine]]"'''


Digital health services empower people to track, manage, and improve their own health and quality of life while delivering a more personalized and precise health care, at a lower cost and with higher efficiency and availability. Essential for the use of digital health services is that the treatment of any personal data is compatible with the Patient Data Act, Personal Data Act, and other applicable privacy laws.
This study seeks to develop, test and assess a methodology for automatic extraction of a complete set of ‘term-like phrases’ and to create a terminology spectrum from a collection of natural language PDF documents in the field of chemistry. The definition of ‘term-like phrases’ is one or more consecutive words and/or alphanumeric string combinations with unchanged spelling which convey specific scientific meanings. A terminology spectrum for a natural language document is an indexed list of tagged entities including: recognized general scientific concepts, terms linked to existing thesauri, names of chemical substances/reactions and term-like phrases. The retrieval routine is based on n-gram textual analysis with a sequential execution of various ‘accept and reject’ rules with taking into account the morphological and structural [[information]].


The aim of this study was to develop a framework for legal challenges to support designers in development and assessment of digital health services. A purposive sampling, together with snowball recruitment, was used to identify stakeholders and information sources for organizing, extending, and prioritizing the different concepts, actors, and regulations in relation to digital health and health-promoting digital systems. The data were collected through structured interviewing and iteration, and three different cases were used for face validation of the framework. A framework for assessing the legal challenges in developing digital health services (Legal Challenges in Digital Health [LCDH] Framework) was created and consists of six key questions to be used to evaluate a digital health service according to current legislation. ('''[[Journal:A legal framework to support development and assessment of digital health services|Full article...]]''')<br />
The assessment of the retrieval process, expressed quantitatively with a precision (P), recall (R) and F1-measure, which are calculated manually from a limited set of documents (the full set of text abstracts belonging to five EuropaCat events were processed) by professional chemical scientists, has proved the effectiveness of the developed approach. ('''[[Journal:Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine|Full article...]]''')<br />
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Revision as of 15:05, 8 August 2016

Fig0.5 Alperin JofCheminformatics2016 8.gif

"Terminology spectrum analysis of natural-language chemical documents: Term-like phrases retrieval routine"

This study seeks to develop, test and assess a methodology for automatic extraction of a complete set of ‘term-like phrases’ and to create a terminology spectrum from a collection of natural language PDF documents in the field of chemistry. The definition of ‘term-like phrases’ is one or more consecutive words and/or alphanumeric string combinations with unchanged spelling which convey specific scientific meanings. A terminology spectrum for a natural language document is an indexed list of tagged entities including: recognized general scientific concepts, terms linked to existing thesauri, names of chemical substances/reactions and term-like phrases. The retrieval routine is based on n-gram textual analysis with a sequential execution of various ‘accept and reject’ rules with taking into account the morphological and structural information.

The assessment of the retrieval process, expressed quantitatively with a precision (P), recall (R) and F1-measure, which are calculated manually from a limited set of documents (the full set of text abstracts belonging to five EuropaCat events were processed) by professional chemical scientists, has proved the effectiveness of the developed approach. (Full article...)

Recently featured:

A legal framework to support development and assessment of digital health services
The GAAIN Entity Mapper: An active-learning system for medical data mapping
Visualizing the quality of partially accruing data for use in decision making