Simulation in manufacturing and business: A review

Copyright @ 2009 Elsevier B.V.This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems

Automated topic analysis for restricted scope health corpora: methodology and comparison with human performance

This paper addresses the problem of identifying topics which describe information content, in restricted size sets of scientific papers extracted from publication databases. Conventional computational approaches, based on natural language processing using unsupervised classification algorithms, typically require large numbers of papers to achieve adequate training. The approach presented here uses a simpler word-frequency-based approach coupled with context modeling. An example is provided of its application to corpora resulting from a curated literature search site for COVID-19 research publications. The results are compared with a conventional human-based approach, indicating partial overlap in the topics identified. The findings suggest that computational approaches may provide an alternative to human expert topic analysis, provided adequate contextual models are available

Collocations of high frequency words in nursing research articles and The Academic Collocation List: Similarities and differences

The main objective of this corpus-based study is to research the most frequent two-word collo-cations in the corpus of nursing scientific articles and compare this newly assembled list of nursing collocations with the Academic Collocation List (ACL). The nursing scientific articles corpus (NSAC) used in this study comprises 1,119,441 words from 262 articles of 10 high-quality journals from the Medical Library Association list which nursing students can freely access. The focus is on noun-noun and noun-adjective collocations. The selected articles were converted into txt files using the ABBYY Fine Reader. WordSmith Tools 7.0 and TermeX were used for noun and collocation extraction. The newly assembled Nursing Collocation List (NCL) and the ACL were compared using Microsoft Excel 2016. A total of 488 collocations were identified in the NSAC and the NCL contains 234 (47.9%) noun + noun and 254 (52.1%) adjective + noun collocation combinations. The most frequent two-word collocation is health care and it appeared 618 times in the NSAC. The ACL (2,469) and the NCL (488) share 123 two-word collocations. Although there are some correspondences between collocations in the two corpora, key nursing collocations with notably higher frequencies are identified in the NSAC (365). Despite the fact that the ACL is the most extensive collocation list across different academic fields and it certainly plays an important role in teaching English as a foreign language, this study suggests that it does not provide key nursing collocations for improvement of nursing collocation competence

Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types. We present a method for characterizing the usage patterns of clinical concepts among different document types, in order to capture semantic differences beyond the lexical level. By training concept embeddings on clinical documents of different types and measuring the differences in their nearest neighborhood structures, we are able to measure divergences in concept usage while correcting for noise in embedding learning. Experiments on the MIMIC-III corpus demonstrate that our approach captures clinically-relevant differences in concept usage and provides an intuitive way to explore semantic characteristics of clinical document collections.Comment: LOUHI 2019 (co-located with EMNLP

The Pro.Bio.Dic. (Prototype of a Bioethics Dictionary) project: Building a corpus of popular and specialized bioethics texts

This paper reports on an ongoing, long-term research project in the field of medical ethics and bioethics conducted by a multidisciplinary team combining medical, linguistic, IT and philosophical research interests: the Prototype of a Bioethics Dictionary (Pro.bio.dic). Having already outlined (Vicentini et al. 2011) the reasons and needs to both redefine and update the lexicographic material available so as to provide a corpus-based collection of the English terms of contemporary bioethics to be published on a web platform, the Pro.bio.dic has now entered the key stage of corpus-building. This stage requires establishing the criteria involved in creating a large, statistically-valid reference corpus of both specialized and popular bioethics texts, to be processed by means of text-mining and machine-learning techniques, and to serve as the basis from which the entries of the electronic online tool described as the Pro.bio.dic will be drawn by means of concordancing software

SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medical specialties, document types, and institutions. We present the following: (1) a survey listing common aspects and lessons learned from previous research, (2) a fine-grained annotation schema which could be replicated and guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. The result of this work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations, and can support a variety of clinical NLP tasks and boost the EHR's secondary use for the Portuguese language

The Pro.Bio.Dic. (Prototype of a Bioethics Dictionary) project: Building a corpus of popular and specialized bioethics texts

This paper reports on an ongoing, long-term research project in the field of medical ethics and bioethics conducted by a multidisciplinary team combining medical, linguistic, IT and philosophical research interests: the Prototype of a Bioethics Dictionary (Pro.bio.dic). Having already outlined (Vicentini et al. 2011) the reasons and needs to both redefine and update the lexicographic material available so as to provide a corpus-based collection of the English terms of contemporary bioethics to be published on a web platform, the Pro.bio.dic has now entered the key stage of corpus-building. This stage requires establishing the criteria involved in creating a large, statistically-valid reference corpus of both specialized and popular bioethics texts, to be processed by means of text-mining and machine-learning techniques, and to serve as the basis from which the entries of the electronic online tool described as the Pro.bio.dic will be drawn by means of concordancing software