On geography and medical journalology: a study of the geographical distribution of articles published in a leading medical informatics journal between 1999 and 2004

BACKGROUND: Studying the contribution of individual countries to leading journals in a given discipline can highlight which countries have the most impact on that discipline, and also give some idea about the geographical outreach of those journals. This paper examines the number of countries that contributed articles to one leading medical informatics journal, Medical Informatics & the Internet in Medicine, and the amount of their contributions between 1999 and the first half of 2004. METHODS: The PubMed citations of all indexed articles from the chosen journal (n = 128) were retrieved online (up to Volume 29, Number 2/June 2004 issue, the latest indexed issue as at 28 January 2005). The country of corresponding author's affiliation for each retrieved citation was recorded. The five-year-and-half corpus of abstracts retrieved from PubMed was further explored using MetaCarta Geographic Text Search . RESULTS: The examined journal has an international outreach, with authors from 24 countries, spanning four continents, contributing to the journal during the studied period. The journal is dominated by a very large number of articles from Europe (81.25% of all articles counted in this study), and in particular from the UK (15.63%) and Greece (15.63%). There were no contributions from Africa or South America. CONCLUSION: A detailed discussion and interpretation of these results and ideas for future analyses are provided. MetaCarta can prove very useful as a bibliometric research tool

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.Comment: Clinical NLP Workshop 201