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(Nada Đerek: „Negdje na jugu cvjetaju rogači“, Planjax komerc, Tešanj, 2019.) Kao jedan od rukopisa nagrađenih na natječaju Fondacije za izdavaštvo FBiH 2019. godine, knjiga čije kritičko iščitavanje je svrha ovog skromnog teksta može se, u žanrovskom smislu, s podjednako ubjedljivim književnoteorijskim argumentima uvrstiti u novele, kao što se može smatrati i kratkim romanom; iako…
In enterprises, data is usually distributed across multiple data sources and stored in heterogeneous formats. The harmonization and integration of data is a prerequisite to leverage it for AI initiatives. Recently, data catalogs pose a promising solution to...
Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.
L. Lyons. (2017)cite arxiv:1708.01007Comment: 26 pages, contribution to the CERN in the Proceedings of the 2015 CERN-Latin-American School of High-Energy Physics, Ibarra, Ecuador, 4 - 17 March 2015.