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- Fagerberg, Jan, et al.
(författare)
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The evolution of Norway's national innovation system
- 2018
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Ingår i: Innovation, Economic Development and Policy : Selected Essays - Selected Essays. - : Edward Elgar Publishing. - 9781788110259 - 9781788110266 ; , s. 316-329
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Bokkapitel (refereegranskat)abstract
- This paper analyses the co-evolution of science, technology and innovation policy and industrial structure in a small, open, resource-based economy (Norway). The contributions of the paper are threefold. First, it develops an evolutionary and historically oriented approach to the study of the development of these policies that may have wide applicability. Second, it focuses on a particular type of innovation, innovation in resource-based activities, that differs in many respects from the more commonly studied case of innovation in 'high-tech' industries. Third, the paper advances our understanding of the roles played by institutions and politics in innovation. Previous work on national systems of innovation has devoted little attention to these matters, possibly because much of this work examines 'snapshots' of various innovation systems at a specific point in time and lacks historical depth.
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- Mowery, Danielle L., et al.
(författare)
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Normalizing acronyms and abbreviations to aid patient understanding of clinical texts : ShARe/CLEF eHealth Challenge 2013, Task 2
- 2016
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Ingår i: Journal of Biomedical Semantics. - : Springer Science and Business Media LLC. - 2041-1480. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Background: The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System. This reference standard can be used to improve patient understanding by linking to web sources with lay descriptions of annotated short forms or by substituting short forms with a more simplified, lay term. Methods: In this study, we evaluate 1) accuracy of participating systems' normalizing short forms compared to a majority sense baseline approach, 2) performance of participants' systems for short forms with variable majority sense distributions, and 3) report the accuracy of participating systems' normalizing shared normalized concepts between the test set and the Consumer Health Vocabulary, a vocabulary of lay medical terms. Results: The best systems submitted by the five participating teams performed with accuracies ranging from 43 to 72 %. A majority sense baseline approach achieved the second best performance. The performance of participating systems for normalizing short forms with two or more senses with low ambiguity (majority sense greater than 80 %) ranged from 52 to 78 % accuracy, with two or more senses with moderate ambiguity (majority sense between 50 and 80 %) ranged from 23 to 57 % accuracy, and with two or more senses with high ambiguity (majority sense less than 50 %) ranged from 2 to 45 % accuracy. With respect to the ShARe test set, 69 % of short form annotations contained common concept unique identifiers with the Consumer Health Vocabulary. For these 2594 possible annotations, the performance of participating systems ranged from 50 to 75 % accuracy. Conclusion: Short form normalization continues to be a challenging problem. Short form normalization systems perform with moderate to reasonable accuracies. The Consumer Health Vocabulary could enrich its knowledge base with missed concept unique identifiers from the ShARe test set to further support patient understanding of unfamiliar medical terms.
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- Suominen, Hanna, et al.
(författare)
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Overview of the ShARe/CLEF eHealth Evaluation Lab 2013
- 2013
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Ingår i: Information Access Evaluation. Multilinguality, Multimodality, and Visualization. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642408014 - 9783642408021 ; , s. 212-231
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Konferensbidrag (refereegranskat)abstract
- Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) in clinical reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients’ information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identified clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.
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